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AWARENESS OF OPEN-SOURCE INTELLIGENCE GATHERING TOOLS BEING USED WITH SOCIAL MEDIA
TABLE OF CONTENTS
Social networking site usage has become an everyday activity and has generated a polarized public debate on whether privacy on social networking sites can exist (Correa, Hinsley, & De Zuniga, 2010). Social networking sites’ lack of privacy rules and constantly changing nature of features has left privacy concerns among Internet users (Jeong & Kim, 2017; Krasnova, Veltri, & Günther, 2012). However, given the diffusion of the social networking sites and increased disclosure of personal information online, the ‘privacy paradox’ suggests that while Internet users are concerned about privacy, their behaviors do not mirror those concerns (Madden, 2012). The privacy paradox highlights the discrepancy between the users’ intentions to protect their privacy and how they actually behave in the online marketplace. Once personal information has been disclosed and made into to public on social networking sites, users’ privacy are definitely at risk.
Open-source intelligence gathering refers to the process of collecting, processing, and analyzing individual information that has been shared on open sources and thus made available to the public. These open-source make use of intelligence gathering tools (IGT) to gather information publicly shared in offline and online sources such as social networking sites (Cutillo, Molva, & Strufe, 2009). IGTs existed since in the mid-1990s; however, most of the Internet users do not recognize the capability of such tools in creating privacy concerns (Steele, 2007). According to Madden et al. (2013), 91% of teens do not express concern regarding the access of third party tools to information posted on social networking sites. For example, if a job seeker sought help in a rehab facility more than 10 years ago and has no criminal record, a potential employer can possibly get hold of that information by just using IGT.
The focus of Chapter 1 is to provide a brief background about the problem, present the problem statement, and state the purpose of the study. In addition, the research questions and hypotheses, theoretical framework, nature of the study, significant of the study, and terms important for the study will be delineated. Assumptions, scope and limitations and delimitations will also be discussed. The chapter concludes with a summary of the important details about the study.
A social networking site, such as Facebook or Twitter, is a platform where the user engages with a chosen audience while social media refers to the information a user uploads to a social networking site (Totten, Phelps, Schuldt, Deville, & Brady, 2016). Social networking sites are particularly vulnerable to attacks on users’ privacy and may allow personal information to be compromised, accessed by the government, searched by enemies or cyberstalkers/bullies, or used for criminal activities, such as identity theft (Fielding & Cobain, 2011; O’Keeffe & Clarke-Pearson, 2011; Raynes-Goldie, 2010). Technological advancements geared towards the faster exchange of information online and communication have fostered the development of social networking sites, which have now become the diners, front porches, churches, courthouse steps, and other gathering places of the twenty-first century (Wellman, 2001). Wellman raised concerns with privacy issues associated with social media, but this study did not focus on the tools used to undermine privacy from the social networking sites.
IGTs can be used to exploit the privacy settings on social networking sites (Miles, 2012; Ying, 2013). Spokeo, which aggregates information readily available on the Internet, provides a service that turns the information into intelligent information, available to anyone at a nominal cost (Pugh, 2017). In the following examples, stalkers, sexual perpetrators, and kidnappers gathered information from social networking sites utilizing IGTs. The information gathered includes an individual’s name, date of birth, location, family names, and other personal information; all of this information can be used by stalkers or criminals to exploit these individuals (Pugh, 2017). Numerous cases of such violations are reported annually to the Working to Halt Online Abuse organization (Hitchcock, 2014).
Miles (2012) reported a specific case where a female model was stalked via a social networking site. The perpetrator was able to use the model’s name and other information from social networking sites and IGTs in order to learn the model’s family members’ names and addresses (Miles, 2012). The predator then sent threatening messages to the model, which stated family members’ names. Additionally, sexual predators provide an example of how social networking sites can attract, stalk, and potentially abuse victims (FOX19, 2012). IGTs can be used to locate home addresses and cell phone numbers (Bose, 2008) allowing predators to show up at a victim’s residence. Lastly, social networking sites and IGT have been used in identifying potential kidnap victims for ransom (Wigginton, Jensen, Vinson, & Graves 2014; Ying, 2013). These examples have demonstrated that information on social networking sites, combined with IGT, can compromise privacy to the point of real harm to individuals.
These examples above showed how IGT could compromise the privacy of users to the point of real harm to individuals. According to Hitchcock (2008), the information posted to social networking sites can potentially affect an individual’s social and emotional lives. Edison Research Group (2012) concluded that 27 out of 100 Facebook users are particularly concerned about privacy, 29 are somewhat concerned, 20 are slightly concerned, and 24 are not concerned at all about privacy. Hugl (2011), stated that adults are more attentive to potential privacy concerns associated with social media versus teenagers. Madden and Smith (2010) presented a contradiction to Hugl, claiming that individuals are becoming less concerned about the amount of information placed on the Internet about them.
The search engine results are useful for gathering information regarding the individual digital footprint. The information an individual posts to their social networking sites builds the accuracy used by an IGT. According to Sweeney (2001), 87% of the residents in the US can be uniquely identified by their five-digit ZIP code, gender, and date of birth. Additionally, Eckersley (2010) noted that 83.6% of browsers tested had at least one of the following unique identifiable features: user agent, plugins, fonts, video, super-cookies, HTTP accept, time-zone, and/or cookies enabled. Data mining can be used to locate this information, and IGTs can then be used to compromise privacy (McCue, 2006; Zhou, Pei, & Luk, 2008).
The general problem is that undergraduate students are not aware that IGTs can be used to exploit public information on social networking sites (Barnes, 2006; Gross & Acquisti, 2005). Lewis, Kaufman, and Christakis (2008) analyzed the Facebook profiles of undergraduate students attending a private American university and found that only one-third of them were private. Compromised privacy in the hands of a stalker or cyber-stalker could be detrimental to an individual’s health or jeopardize their safety and college students are not immune though they may think otherwise (Haron, 2010). An awareness of IGTs is critical to personal privacy and security (Barnes, 2006; Gross & Acquisti, 2005).
The potential vulnerability of exploiting public information posted by users on social networking sites through the use of IGTs, raises a need for this study to be conducted (Barnes, 2006; Boyd & Ellison, 2008; Dwyer, Hiltz, & Passerini, 2007; Fogel & Nehmad, 2009; Gross & Acquisti, 2005; Haron, 2010; Lewis, Kaufman, & Christakis, 2008; Livingstone, 2008; Thelwall, 2008; Young & Quan-Haase, 2009). Researchers have raised privacy concerns associated with the use of social networking sites. Privacy is one of the most critical issues facing users of social networking sites (Kaplan & Haenlein, 2010; Strater & Richter, 2007). In this quantitative study, the author will look to address the problem of open-source IGTs used to exploit public information gathered from social networking sites (Beer, 2008). This problem has impacted individuals unaware that their public information was vulnerable (Raynes-Goldie, 2010). Breaches of privacy have, in some cases, result in physical damages and even the loss of life (Haron, 2010).
The purpose of this quantitative correlational study is two-fold. First, is to examine the predictive relationship between users’ knowledge of open-source IGTs and the likelihood that the users will upload personal information to social networking sites. Second, is to examine the moderating role of users’ demographic factors (gender, academic standing, and age) on the predictive relationship between users’ knowledge of open-source IGTs and the likelihood that the users will upload personal information to social networking sites. The target population for this study is comprised of undergraduate students at a four-year college in Daytona Beach, Florida. The predictor variable is the users’ knowledge of open-source IGTs, the criterion variable is the likelihood that the users will upload personal information to social networking sites, and the moderating variables are the users’ demographic factors. A self-developed survey will be used to measure the study variables.
This study is significant because the results can potentially provide academic institutions and college students’ knowledge to offset the potential risks associated with the capacity of IGTs to compromise users’ privacy on social networking sites. The study will potentially generate information concerning the proper use of social networking sites by college students, as well as their awareness of the capacity of IGTs to exploit privacy settings on social networking sites. Furthermore, this study will potentially provide awareness to media users. Lastly, this study will potentially provide future researchers with baseline knowledge concerning the use of IGTS and the effects on privacy.
The advantages of Internet-based information exchange cannot be discounted, and thus both businesses and consumers still prefer online communication and transactions while leaning towards an online environment that is not only convenient and in real-time but also secured. Security measures are a priority for businesses and consumers alike when engaging in online transactions and communication, and identifying how to mitigate the risks involved is of utmost importance to all. Given that this study aims to identify whether the knowledge of IGTs as well as the selected demographic variables are associated with likelihood of that the users will upload personal information to social networking sites, the results of this study may be relevant and of importance to mitigate the risks involved in Internet-based information exchange, thus reducing the likelihood of criminal activities, such as identity theft (Fielding & Cobain, 2011; O’Keeffe & Clarke-Pearson, 2011; Raynes-Goldie, 2010).
The proposed study will use a quantitative correlational research design. The research method for this study will be quantitative in nature. Researchers use quantitative methods to test hypotheses using numerically measured variables subjected to statistical analysis (Simon, 2011; Watson, 2015). Quantitative methods are normally used when the objective of the research is to examine the relationships between variables and make inferences about the population under study using the statistical results (Barczak, 2015; Simon, 2011). In quantitative research, variables are pre-specified by the researcher which means the definition and operationalization of the variables are known before any data collection commences (Watson, 2015). Quantitative studies delineate the pre-specified variables into either predictor or criterion variables, where the former being the variable that is changed or controlled while the latter being the variable being tested and measured in research or an experiment (Cooper & Schindler, 2013). In some studies, moderating variables are included which is a variable that affects the strength of the relationship between the predictor and criterion variables (Watson, 2015).
This research design for the study will follow a correlational design. The strength of correlational research is its predictive capabilities. The quantitative correlational research aims to investigate and explain the nature of the relationship between two or more variables (a similarity between them), by collecting data on existing variables and examines relations between those variables (Babbie, 2013; Rottman & Hastie, 2014). The basis of using correlations in this study is to figure out which variables are connected and therefore address the research questions and hypotheses of the study. Compared to other designs, correlational research design is used because it describes or predicts behavior, not explain it (Bosco, Aguinis, Singh, Field, & Pierce, 2015). Also, it is more reliable and objective in terms of describing the relationship between two or more variables (Rottman & Hastie, 2014).
The research questions and hypotheses that will guide in addressing the purpose of this study are:
RQ1. To what extent the knowledge of open-source IGTs predict the likelihood that the users will upload personal information to social networking sites among undergraduate students at a four-year college in Daytona Beach, Florida?
H10: There is no significant predictive relationship between knowledge of open-source IGTs predict the likelihood that the users will upload personal information to social networking sites among undergraduate students at a four-year college in Daytona Beach, Florida.
H1a: There is a significant predictive relationship between knowledge of open-source IGTs predict the likelihood that the users will upload personal information to social networking sites among undergraduate students at a four-year college in Daytona Beach, Florida.
RQ2. To what extent the demographic factors (gender, academic standing, and age) moderates that predictive relationship between the knowledge of open-source IGTs and the likelihood that the users will upload personal information to social networking sites among undergraduate students at a four-year college in Daytona Beach, Florida?
H20: Gender does not significantly moderate the predictive relationship between knowledge of open-source IGTs predict the likelihood that the users will upload personal information to social networking sites among undergraduate students at a four-year college in Daytona Beach, Florida.
H2a: Gender does significantly moderate the predictive relationship between knowledge of open-source IGTs predict the likelihood that the users will upload personal information to social networking sites among undergraduate students at a four-year college in Daytona Beach, Florida.
H30: Academic standing does not significantly moderate the predictive relationship between knowledge of open-source IGTs predict the likelihood that the users will upload personal information to social networking sites among undergraduate students at a four-year college in Daytona Beach, Florida.
H3a: Academic standing does significantly moderate the predictive relationship between knowledge of open-source IGTs predict the likelihood that the users will upload personal information to social networking sites among undergraduate students at a four-year college in Daytona Beach, Florida.
H40: Age does not significantly moderate the predictive relationship between knowledge of open-source IGTs predict the likelihood that the users will upload personal information to social networking sites among undergraduate students at a four-year college in Daytona Beach, Florida.
H4a: Age does significantly moderate the predictive relationship between knowledge of open-source IGTs predict the likelihood that the users will upload personal information to social networking sites among undergraduate students at a four-year college in Daytona Beach, Florida.
The predictor variable is the users’ knowledge of open-source IGTs, and the dependent variable is the likelihood that the users will upload personal information to social networking sites. Moderating variables are the demographic factors of gender, academic standing, and age. All variables will be measured using a self-developed survey that will be discussed further in Chapter 3.
The routine activity theory (RAT) is the basis for this study and is used widely in criminology (Miro, 2014). According to RAT, trends in crime relate to daily activity and crime depends on the presence of an aggressor and a target. Crime opportunities arise from the factors related to the aggressor and the target. The absence of either the aggressor or the target would prevent crime (Cohen & Felson, 1979). Researchers use RAT to examine criminal activities at the macro level and the factors that enable such activities. Thus, RAT will be a useful theoretical lens for viewing the likelihood of exploiting user’s public information in social networking sites given that it is a macro-level criminal activity affecting many people. The researcher aims to reduce the number of targets or possible victims of criminal activities and mitigate the concerns of privacy risks in an online environment by determining the how the knowledge about IGTs and other selected demographic factors of users contribute to the likelihood that the user will upload personal information to social networking sites.
The researcher advocates focusing efforts on removing the victim from the equation, instead of focusing on the aggressor because the Internet has blurred geographical boundaries between nations, allowing cyber crimes to be conducted anywhere in the world (Manap, Rahim, & Taji, 2015). Therefore, implementing guidelines against criminal activities online may not be as effective as expected. Instead, efforts to educate potential victims may be more effective in helping to reduce instances of cybercrime victimization, particularly for privacy risks or even online identity theft (Tajpour, Ibrahim, & Zamani, 2013).
The purpose of this study supports the need to provide more empirical evidence about the growing problem of identity theft victimization (Langton & Baum 2010; Smith, 2010). According to the Federal Trade Commission (FTC 2010), the identities of over 9 million U.S. citizens are stolen each year, with a median cost to victims of $500. Further, the recent survey conducted by the National Crime Victimization Survey reported that 6.6% of all U.S. households included a victim of one or more types of identity theft, an increase of 23% since 2005 (Langton & Baum, 2010). Existing research has focused on online routine activities in determining the various online forms of victimization (e.g., Choi, 2008; Holt & Bossler 2009). However, only a few studies to date have empirically investigated identity theft victimization from the RAT perspective (Reyns, 2011). Of those studies examining fraud victimization, a related crime, only a few have shown how it can correlate with the increase of identity theft victimization; but they have not identified specific demographic variables of potential victims. As such, the use of routine activity theory for this study can help explain the circumstances under which opportunities for criminal victimization occur by looking at the knowledge of users about IGTs and users’ demographic factors.
Intelligence: a type of privileged information, and the activity of acquiring, producing, and possibly acting on the information (Warner, 2009).
Open-source intelligence gathering: the collection and management of sources fused with the validation of the source of information (Steele, 2007).
Open-source information (OSIF): both printed and non-printed materials (Steele, 2007).
Open-source intelligence (OSINT): any OSIF that has been discovered, discriminated, and disseminated to produce intelligence (Steele, 2007).
Open-source software (OSS): software for which the source code is publicly available under a license; the developer withstands the copyright and provides right for any individual to examine and alter the source code (Laurent, 2004).
Privacy: information that an individual does not wish to make known to other individuals (Bier, 1980; Pennock & Chapman, 1971; Westin, 1967).
Social media intelligence (SOCMINT): the wide range of applications, techniques, and capabilities used to collect social media data (Omand, Bartlett, & Miller, 2012).
Social media presence: a public or semi-public profile with a closed system; individual user connection must be articulated in a list; allow social media users to view and transverse their list of connections and the list of connections of other users (Boyd & Ellison, 2008).
Social media: the collection of Web 2.0 technology that allows users to exchange and create user-generated content (Kaplan & Haenlein, 2010).
Social networking site: a website of public or semi-public profiles within a confined system which with a list of the profile connections and the ability to traverse other connections within the confined systems (Ellison, B. 2007).
Validated open-source intelligence (OSINT-V): any open-source information with exceptionally high certainty-attributes produced by intelligence professionals with access to classified intelligence sources and is validated (Steele, 2007).
Every research requires some assumptions to be made due to the fact that quantitative study aims to make inferences about unknown population parameters (Vogt, 2011). There are two main assumptions for this study. First, it is assumed that the use of a selected four-year college in Daytona Beach, Florida than the entire population of undergraduate students in Daytona Beach, Florida will produce more valuable and cohesive insights on the targeted population. Such assumption follows the fact that the researcher cannot get relevant information and permission from other four-year colleges within the chosen geographical area because of time and logistic constraints. Second, it is assumed that the participants for the study will answer every question in the survey truthfully. The survey is simple for the participants to follow but the researcher will still explain and include all possible instructions in order to ensure that everything is understandable for the participants.
A limitation of this study is that the survey will poll undergraduate students only from a four-year college in Daytona Beach, Florida, and therefore the sample may not represent the entire undergraduate student population. Participants are a volunteer, and the survey itself is self-reported. Given that the survey will be administered online, the researcher cannot control whether the respondents, in fact, have Internet access with which to take the survey. The researcher cannot control the number of respondents from the sample population; however, with survey reminders, the response rate can be impacted.
This research may be limited due to researcher’s bias. This is due to the fact that the conclusions derived from the results of a quantitative study are dependent on the researcher’s interpretation of applicable literature. As such to adjust for such kind of biases, it is important that the literature review is comprehensive and all insights will be supported by relevant citations. Another limitation is that results from correlational studies are not definitive in causation between the study variables. Babbie (2013) asserted that there could be several reasons why variables behave the way they do. It is only through experimental study; a researcher can have certainty regarding the true causation between variables as such design involves random assignment, which correlational studies do not possess (Rottman & Hastie, 2014).
Delimitation is a factor that the researcher intentionally imposes to constrain the scope of the study to make it manageable (Vogt, 2011). The study will be delimited to only one four-year college at Daytona Beach, Florida thus limiting the demographic sample. Though there is a total of more than one four-year college in Daytona Beach, Florida, the chosen one have the most number of undergraduate students, which may give a better chance for this study in getting diverse views and experiences from undergraduate students. The researcher wanted to have more in-depth insights from the relevant population rather than choosing a wider population but a higher possibility of inconclusive insights. Other factors that may contribute to the likelihood that the users will upload personal information to social networking sites such as a number of social media accounts, time spent in using social media, and race among others will not be considered for the study.
Chapter 1 included an introduction to the problem of open-source IGTs and the use of social media to compromise privacy. This chapter contained examples of how social media and open-source IGTs can be used to compromise individual privacy. Additionally, the chapter included definitions associated with the study. Lastly, the chapter contained the assumptions and limitations associated with the study.
Chapter 2 includes a comprehensive literature review pertaining to the history of social media, open-source IGTs, and privacy associated with IGTs and social networking sites. The author incorporates political, economic, social, technological, and legal (PESTL) analytical techniques into the literature review to investigate the macro-level environmental impacts IGTs and their capacity to exploit the privacy of users on social networking sites potentially. Through analysis of literature regarding IGTs and their capacity to exploit privacy, the author suggests that social network users are not aware of potential threats IGTs pose to their online privacy. The results of this quantitative study will provide college-aged (18-25 years) students who use social network site with awareness and data for evaluating the risks IGTs pose to their privacy on social media sites. Lastly, in Chapter 2 the author outlines the current research regarding IGTs, privacy, and the gaps within the literature.
Social media, despite surfacing after the Internet became public in 1991, still has a relatively young history. The use of social media will continue to grow as each generation increases in physical isolation from others. This physical isolation is a result of new technology, which facilitates easy access to social media—thus eliminating the need to leave the house and interact with others for recreation or information. According to Bartlett and Miller (2013), 1.2 billion individuals use online social media sites, apps, blogs, and forums to exchange and view information. There are several examples of these different types of platforms; some include LinkedIn with 200 million users, Russian-language network 190 million users, Chinese QQ 700 million users, Reddit 400 million unique visitors in 2012, and Tumblr with 100 million blogs. These platforms account for a significant amount of time spent online (Bartlett & Miller, 2013).
Boyd and Ellison (2008) asserted that the first modern, web-based social media site was SixDegrees.com in 1997. The next online social media site was Ryze.com (Boyd & Ellison), a product of the San Francisco business and technology community. These same community leaders later funded and supported other online social media network sites such as LinkedIn and Friendster (Festa, 2003). According to Boyd and Ellison, the three most prominent online social media sites are Friendster, MySpace, and Facebook.
In 2002, Friendster was founded to compete with Match.com and as a complement to Ryze.com (Boyd & Ellison, 2008; Cohen, 2003). Friendster was immensely popular among three social and lifestyle groups: bloggers, attendees of the Burning Man arts festival, and alternative lifestyles (Boyd, 2004). In the infancy of Friendster, the total number of users worldwide reached 300,000 (O’Shea, 2003). With the growth of users and media coverage, Friendster network structures and resources became strained. This strain caused the site downtime and disappointed users (Boyd, 2006a). Additionally, this growth started the privacy discussion on the online social networking sites. Users were now faced with employers, family, and strangers being able to view their social media posts (Boyd & Ellison, 2008).
Friendster started implementing privacy settings and restricting users’ activities. The first restriction was that users could not view other users’ profiles that were more than four degrees away from their personal profile (Boyd & Ellison, 2008). Four degrees away can be interpreted as a friend of a friend to the second power. These four degrees of separation caused the problem of “Fakesters,” which are people with fake accounts on social media sites (Boyd & Ellison, 2008). This problem caused Friendster to eradicate both real and fake users’ accounts through active deletion, which led to fewer users using Friendster in the United States. Additionally, Friendster eliminated its “most popular” feature, which encouraged the mass collection of friends (Boyd & Ellison, 2008). Although Friendster’s popularity declined in the United States, the company’s presence grew in Southeast Asia. This growing popularity in Southeast Asia was caused by the company’s headquarters moving from Mountain View, California, to San Francisco—a city with a large Asian-American population. These early adopters soon invited their friends and families overseas to join Friendster in order to keep in touch with them—and Friendster’s reach expanded to Southeast Asia (Liu, 2008). Friendster problems of Fakester users is still a problem with online social media (Gross & Acquisti, 2005).
In 2005, the News Corporation purchased MySpace for $580 million. This purchase made MySpace gain national attention, which caused privacy issues for MySpace (BBC, 2005). The social media site was associated with several cases of child grooming and sexual predators, which started to become a public concern (Bahney, 2006).
In 2004, Facebook launched as a Harvard-only social network site, and the only way to join was with a Harvard.edu email address (Cassidy, 2006). With the growth of Facebook, the company allowed other students from universities and colleges to join (Boyd & Ellison, 2008). In the fall of 2005, Facebook allowed everyone else to access the social media network. Users who wished to join a high school network required an administrator approval (Boyd & Ellison, 2008). The features of Facebook set them apart from other social media sites. Unlike Friendster and MySpace, Facebook did not allow their users to make their profiles public to all users. Facebook allowed for the use of applications to run on their site. These applications allowed users to have more interactions with other users (Boyd & Ellison, 2008).
Twitter is the richest and most heavily researched online social media site (Bartlett & Miller, 2013). Twitter has been in operation since 2006 and currently has 313 million monthly active users (Twitter, 2016). According to Smith (2014), there are over 100 different languages used on Twitter. English accounts for about 50 percent of tweets, and Spanish, Mandarin Chinese, Japanese, Portuguese, and Indonesian languages account for 40 percent of tweets.
Social media is a global trend; according to Ellison,Lampe, and Steinfield (2007), social media has now started in Asia. China has a variety of social media, including Sina Weibo, RenRen PengYou Tencent Weibo, and Tencent QQ (Meredith, 2013). QQ has similarities toSkype, but also offers features such as blogs, games, email, and online payments (Linkfluence, 2016). Skype is a Voice over Internet Protocol (VoIP) calling service and instant messaging service. Skype, Facebook, Twitter, YouTube, and Google are blocked by the Chinese government (Moore, 2010). According to the Tencent QQ website, 158,274,490 users are currently online (Russell, 2012).
In 2012, South Korea launched CyWorld which is remarkably similar to Facebook but has an animated feel, which consists of avatars of the users. According to FreedomHouse.org, which analyzes the freedom of the Internet in countries globally, as of 2012, Facebook and other social media sites are blocked in South Korea (Mortensen & ProQuest Information and Learning Company, 2012).
In Iran, all citizens are barred from all social websites by the Iranian government. The Iranian government is currently developing intelligent software that will capture, monitor, and analyze all social media usage of their citizens (Ahy, 2016; “Tightly-controlled social media,” 2013). As a result of the social media revolution, the Iranian government launched the cyber police (FATA). This police agency’s sole purpose is to monitor all online conduct of anti-regime opposition. The seriousness of objections to this regime can be illustrated by the story of a blogger named Sattar Beheshti, who was arrested by FATA for the crime of anti-regime rhetoric on social networks and Facebook (“Iranian blogger who,” 2012). According to Kaleme.com, an Iranian news outlet, Sattar Beheshti died while in custody after being tortured by the FATA. The following is part of the blog that Sattar Beheshti posted that led to his death (“Iranian blogger who,” 2012):
“Mr. Khamenei, [in Iran] people, are arrested and are then subjected to a series of torture until they confess to crimes they did not commit. Burning cigarettes are extinguished in their flesh; they are branded with hot irons, their heads are shoved into toilets full of excrement until they confess to a sin they never committed… After the passing of a sentence based on this confession, the laws of Shari’a and God’s religion are implemented!… Is this [indeed] God’s religion – or is it [merely] violent behavior in the name of God’s religion, with the aim of remaining in power? …As an Iranian, I see you and your judicial system – your slaughterhouse – as artists in the art of murder. Believe me [when I say] that I think you are an artist”.
The Iranian government has an exceptionally radical visibility and control over the use of social media in their country. Similarly, journalists and bloggers in Saudi Arabia, Egypt, Lybia, Syria, and Yemen have faced serious negative consequences for expressing their independent views (Ghannam, 2011).
Bahrain citizens are prevented from using social media. In 2002, the Bahrain Ministry of Information implemented the 2002 Press Law, which led to the blocking of over 1,000 websites (“Saudi Arabia freedom,” 2012). These blockages also extended to individual pages on social media websites (“Ministry of Information,” 2009). The Bahrain government uses software to conduct online surveillance of online activity, as well as to monitor the phone calls of their citizens. Furthermore, government officers at security checkpoints actively search content on mobile devices for anti-government content (“Political media in,” 2012).
Kuwait citizens are blocked from using social media; this includes voice over internet protocol (VoIP) services such as Skype, which is blocked in most Arab countries (Kadi, 2007). The Kuwait Ministry of Communication requires the internet service providers (ISP) to block pornographic material, homosexual content, anti-religion, anti-tradition, and anti-security websites. The Kuwait Ministry of Communication asserts that the blockage of this content is to protect public order and morality (“State of the” 2013).
Oman, Qatar, and the United Arab Emirates citizens do not face as ardent censorship as citizens in Bahrain and Kuwait. The revolution is known as the “Arab Spring” has brought considerable attention to the countries of Oman, Qatar, and the United Arab Emirates and the use of social media. Social media in these countries have been used to communicate and educate citizens openly to criticize ruling families and government corruption. Additionally, social media have been used to emphasize the need for political reform. Lastly, the residents should be aware that changes in these countries’ social media policies are dynamic and could change at a moment’s notice (Davidson, 2012).
The popularization of online social networking sites has changed the privacy expectations and rights of college students (Quan-Haase, 2007). The privacy issue is that social networking sites allow individuals to present themselves as favorably as possible through online profiles in order to make more connections, which in turn encourages them to share personal information such as hobbies, interests, music tastes, and relationship status without giving much thought to their privacy (Ellison et al., 2007). This sharing of personal information on social networking sites with a lack of privacy concerns has caused problems for some college students. These problems include difficulties finding employment due to the potential employer’s access to an individual’s unfavorable social media profile, law enforcement learning of and disrupting social gatherings, as well as the disqualification from being able to run for political office due to opinions or information posted on social media which can be viewed as unacceptable by the public (Finder, 2006; Hass, 2006; Santora, 2007). Additionally, lack of privacy can enumerate several other risks, including embarrassment, blackmail, stalking, and possibly identify theft (Acquisti, 2005). A study conducted by Lewis, Kaufman, and Christakis (2008) which surveyed 1,710 college undergraduates Facebook profiles revealed that the mean for profiles set to private was 33.2% (568). This small mean demonstrates the lack of concern regarding privacy among college students Facebook profiles.
Analyzing privacy concerns by using social capital is one of the viable approaches to identifying the surrounding issues involved. Social capital on a broad scale refers to the resources that are accumulated because of relationships between people who are relating on an individual network (Bourdieu, 2001). Putnam (2000) identified two other forms of social capital:bridging social capital, which consists of relationships created out of weak ties between individuals and bonding social capital that is derived out of intimate or close relationships between individuals.
As a broad concept, social capital mainly refers to the resources achieved through relationships (Coleman, 1988). Bourdieu and Wacquant (1992) defined social capital as the “sum of the resources, actual or virtual, that accrue to an individual or a group by possessing a durable network of more or less institutionalized relationships of mutual acquaintance and recognition” (p.14). Based on the nature of the relationships themselves, these relationships can differ in nature as well as in functionality.
Social capital over the years has been realized to contain within it positive as well as negative value. Social capital is the goodwill—sympathy, trust, and forgiveness—offered to us by those around us, on which we can capitalize by promoting action towards collective goals and well-being. For example, positive social capital can lead to reduced crime prevalence rates, efficient financial markets and improved public health rates (Adler & Kwon, 2002). Adverse aspects of social capital are mainly noticed, according to Putnam (2000), when resources are reduced, and it is displayed through increased social disorder, as well as reduced activity and participation in community-based activities such as clean up exercises and community awareness programs (Putnam, 2000).Therefore, with an increase in social capital, there is potential for individuals to interact more efficiently, bringing about increased participation in community activities in the communities in which they live. Social capital resources, therefore, reveal the aspect of commitment towards each other in the community through the sharing of resources.Social capital has been realized to bring about positive changes within a community and society (Helliwell & Putnam, 2004).
Social capital resources are achieved mainly through the exchange of useful information, building of personal relationships, as well as the capacity of individuals to work together as a team (Paxton, 1999); however, social capital also gives people access to useful source information, especially for those relationships that have been built together through close ties such as close friends (Granovetter, 1973). Close relationships psychologically have been identified as being a vital source for the well-being of individuals today, as they boost individuals’ self-esteem levels as well as provide them with a sense of purpose (Helliwell & Putnam, 2004).
Two different types of social capital relationships are built based on bridging and bonding social ties. The bridging ties, according to Putnam (2000), are constructed upon weak ties and constitute connections between people based on mainly common ground or mutual understanding in which the participants in the relationship have expectations of a conventional nature. For example, relationships based on acquaintances could assist in one obtaining employment if one is unemployed. The weakly tied relationships, therefore, are founded on the concept of sharing information that is useful to each other but is not influenced in any way by emotion (Granoveter, 1982). Bonding social capital, on the other hand, is based on relationships founded on emotion. These relationships could take on the aspects of family-based ties or close friend ties. In these relationships, the information conveyed between the participants is loosely defined as the exchange of redundant information.
Granovetter (1973) asserted that through the bridging of social capital, the weak links associated therein and the lack of familiarity of the people leads to the participants sharing mainly vital information. Additionally, for maximum diffusion of information, Granovetter suggests that weak ties are more effective than strong ties. This is due to the fact that strong ties often share the same social circle—keeping the information within these close, overlapping circles of friends. In contrast, when information is shared between weak ties, these individuals will then share it with their respective social networks—this disseminating the information much further (Granovetter, 1973).
Whereas weak ties are generally used for spreading useful information, this is not the case between strong-tie relationships, otherwise referred to as bonding social capital, in which redundant information is generally shared. This is due to the fact that the same information travels within the social circle of strong-tie relationships (Granovetter, 1973). In the bonding social capital scene, the relationships are primarily founded on trust, reciprocity, and emotional support (Putnam, 2000). The relationships based on the above principles of bridging and bonding ties are now being formed and developed both physically as well as virtually. Physically, they are built with people holding meetings and conversing in person, whereas virtually this includes the communication of people through technology such as through telephones, and over the Internet.
Nie (2001) argued that as a reducing agent, the Internet has contributed towards people interacting less because of the lack of face-to-face time between individuals. The lack of face-to-face time has acted to diminish the relationships between individuals, as some do not value this aspect of communication. Researchers argued that the Internet has strengthened the relationships between individuals, as it has become a supplement to, or even replacement of, in-person interactions which effectively lead to relationships being maintained when physical constraints are present, such as geographical displacement (Wellman, Haase, Witte & Hampton, 2001). Most research, however, supports the claim that instead of diminishing social capital, the Internet and the technological advancements made have indeed helped strengthen the relationship bonds between individuals (Hampton & Wellman, 2003; Kavanaugh, Carrol, Rosson, Zin, & Reese, 2005).
Weak ties are mainly created through the Internet, while strong ties are maintained through the Internet. The Internet linkage between the relationships, therefore, supports the creation and maintenance of both types of social bonds that are available. Bridging social capital created the possibility that loose social ties will be created, which will allow users to set up and maintain larger, more diffused networks from which they possibly can draw resources (Donath & Boyd, 2004). Furthermore, the Internet has the potential of increasing social ties, especially weak ties, between individuals as the Internet is well prepared to perform this efficiently and cheaply (Donath & Boyd, 2004). The Internet facilitates new connections between people in which they can obtain sources to useful information depending on the nature of the interests of the fellow users on the social sites. Therefore, this can lead to an increase in social capital for the users.
The literature on bonding social capital questions whether the Internet serves the role of creating or forging new relationships. Williams (2006) pointed out that there has been little empirical research aimed at analyzing if the Internet supplements close ties; however, it is clear that the Internet does indeed serve to promote and maintain social capital, especially that which has been compromised by physical barriers as family and close friends can maintain contact over large geographical distances.
The Internet does indeed play a decisive role in creating relationships between individuals. Individuals may find it difficult to establish ties with others offline, but with the help of online media tools and forums such as the social networking sites, it is easier for them to develop relationships–which consequently adds to their well-being (Bargh & McKenna, 2004). Online media tools and personal problems, such as the inability to maintain good relationships, or lack of self-esteem, can make the Internet a place where these individuals can solve their issues as they get to interact with others over the available platforms. Over such platforms, these issues can be solved through the possibility of new relationships being formed within these forums (Bargh & McKenna, 2004; Tidwell & Walther, 2002).
Social networks have evolved as different relationships were formed while others were broken. The changes in social networks have been widely affected by the changes that are being witnessed in individuals’ changes in their social capital resources. The changes in their social capital are results of moving away to a new geographical area, which forces the communication methods between the parties to change. Putnam (2000) suggested that declining social capital is mainly caused by families moving from one area to another for whatever reason. In the past, use of emails was the norm where connections barred by distance could maintain their relationships;however, this was changed with the emergence of social media sites such as Facebook (Wellman et al., 2001).
Strahilevitz (2004) regarded privacy as a human right to keep sensitive information, such as that concerning sexuality, medical ailments, and past misdeeds to name a few, confidential. With privacy exists the expectation that when one shares intimate details with another person, that person will not disclose that confidential information to anyone else. The more private information that an individual is willing to share with another, the closer these two people becomes. Individuals are not willing to make all of their information public and tend to hold it in secret or share it only with those with whom they are intimate. Other situations in which individuals may share their private information with others include rehabilitation and treatment programs in which individuals meet and discuss their personal issues with strangers for comfort and support.
Social media have evolved to be an immense resource through which students and adults could maintain, as well as nurture, relationships with each other that vary in ties. Users’ engagement on the sites involves the dissemination of information about themselves, mainly through the different avenues available on the social sites such as status updates, blogs, videos, and photos. Privacy concerns are raised as a result of the structural capabilities of the social networking sites, such as whether or not it is right for this kind of information to be shared on these platforms (Strahilevitz, 2004).
The issue was mainly raised because of awareness that social media platforms were inadvertently poised to harm individuals because of the information that was disclosed therein, and that this information could be harmful to certain audiences. This information can be accessed and viewed by potential employers, possibly harming an individual’s reputation. Additionally, this information can be used for identity theft (Barnes, 2006; Gross & Acquisti, 2006a). With most social sites, it is a requirement that private information is presented to make it possible for individuals to access these social sites. Social sites such as LinkedIn require that a person provides a comprehensive career background, as it is primarily structured to make it possible for career people to communicate and socialize with each other (Donath & Boyd, 2004). Social sites such as those mentioned above recommend that people share their information by disclosing it to make it more possible for others to be aware of one’s interests.
Information accessibility is possible on social sites as well as other technologies. With the advancements in social media, individuals are always being encouraged to share information that would in the past have been considered as private, such as relationship status and geographical location (Hannay & Baatard, 2011). Most of the information being made public is being disclosed openly on the media platforms with little regard to the potential privacy impacts that they could have on the individuals, even as they disclose this information. Social media has mostly promoted the collection of this data especially through sites such as Twitter, Facebook, and Google +. These services allow for the information gathered to be shared in the form of messages, pictures, and videos over the platforms. Many users of social media willingly accept to use these features either by choice or ignorance (Lindqvist, Cranshaw, Wiese, Hong, & Zimmerman, 2011).
Technological advancements such as the Global Positioning System (GPS), as well as location-based services found on smartphones, cameras and even in iPods, have led to information regarding individuals whereabouts being disseminated. (Hannay & Baatard, 2011). Mobile applications will ask for location settings to be activated, and many individuals unknowingly or willingly will switch on these information collection devices, thereby submitting data that they do not fully understand (Junglas & Watson, 2008). Users are aware of the capabilities that mapping and navigation programs offer, such as in camera functionality, but users may not realize the capabilities of the information gathered that are used in ways other than those intended (Wagner et al., 2010).
The concept of “geotagging” is quickly becoming one that every user of social media networks and technologies such as cameras and smartphones should be aware of. This concept allows for the use of the user’s information about location, to be published alongside the information that they are willingly sharing with the online community (Lindqvist et al., 2011). “Geotagging” originated with high-end digital cameras. The feature allows for the devices and photos or videos that they receive to be integrated on a GPS platform thereby the content is then encoded with the location’s information where the pictures or videos were taken (Hannay & Baatard, 2011). Geotagging has been added to modern social media services applications that allow the application to submit individuals’ geolocation content. In images, the location details are stored in Exchangeable Image File Format (EFIX) information in the camera’s picture files. The EXIF data stored records information about make and model of the device taking the images, the settings of the device, and geographical location (Valli & Hannay, 2010).
These features are commonly available on smartphones and cameras; they can, however, be enabled or disabled in some devices such as Android phones. Many of today’s smartphones come with the devices switched off as the default setting, but many users turn them on without thinking much into it. They are not aware of the real ramifications of their actions (Hannay & Baatard, 2011). One application associated with smartphones is Foursquare, a social media site that is mainly dependent on the location of the user. The Foursquare mobile application gives the users the option of identifying their location in textual information. This is referred to as check-in on the Foursquare application, which will readily collect geographical information and share the user’s location on Foursquare (Hannay & Baatard, 2011). The information above depicts social sites that collect and record information through geotagging, in which the location of individuals is cleverly monitored with the users willingly and unwillingly giving up their rights to privacy.
Geo-Intelligence is the mining of geotagged information that has been made available on social media content (Hannay & Baatard, 2011). This data is accessed through the application program interfaces (APIs) that are connected to websites and which the developers largely use to develop their platforms and applications. The APIs reveal how many individuals have been to a certain area in the past (amount of time), or how many individuals approximately were at a certain event (Hannay & Baatard, 2011). The use of information retrieved from the API does not in any way breach the terms of service stipulated when a user is signing up for the service, as they have already given up their rights to privacy.
Social media service APIs use two ways to mine for data. The first is writing code, which makes queries to the service, and attempts to find information by searching the content stored on the service. The second is retrieving all geotagged content from the service and storing it in a particular database (Hannay & Baatard, 2011). These two distinct features are associated with social media services APIs, which perform keyword searching and data mining (Hannay & Baatard, 2011). The data mining method, however; does not have to use keyword searches as it allows for unlimited searches which make the process easier (Hannay & Baatard, 2011).
The potential users of geo-intelligence services include law enforcement agencies, business intelligence, and marketing companies, as well as privacy awareness and education institutions. For example, retrieved information can be used to identify potential witnesses to a crime that occurred at a particular place and within a specific timeframe (Hannay & Baatard, 2011). Additionally, the information can be used to locate and identify suspects to a crime by examining the posts of certain users and determining the routines of the individuals. For companies, the information given through these social sites could be used to identify interests of the people, locations frequented, and demographics of users who mainly post from certain business premises.
The above ramifications of sharing information publicly on social sites reveal the proper ways that the information could be used (Hannay & Baatard, 2011). There are, however, negative uses as well, such as harassment of individuals through online platforms, and burglary, among others. Geo-Intelligence, therefore, demonstrates how people’s private lives can be made public with or without their consent (Hannay & Baatard, 2011).
Social media sites differ from other websites in three ways. The first is the ability to view one’s own list of connections, as well as the connections made by those connections. The second is a user-constructed public profile. The third is that the sites offer the user the ability to create connections within the system (Boyd & Ellison, 2008). Individuals on social media sites display their profiles to the public and, in addition, are at liberty to select what can be viewed by other users in the social network. Facebook users can choose to hide their profile from the larger public and specify that only their connections can view what they display. The decision to make connections with other users is left to the discretion of the user (Donath & Boyd, 2004).
The information privacy paradox states that despite individuals’ concerns about the privacy of their information on social media, they do not adjust their user behavior to address such concerns. It is believed that this paradox stems from individuals’ lack of risk awareness, tendency to underestimate the privacy dangers of self-disclosure, as well as a lack of understanding of ways to protect their personal information (Taddicken, 2013). However, other research has shown that the interaction of the users with other users also shows evidence that the more variations are present between the users’ audience or connections, the more the users have adopted strategies with which they interact with their connections (Ellison, Vitak, Steinfield, Gray, & Lampe, 2011). The possible strategies users could use to make sure that their privacy is well protected would be by using pseudonyms or using the advanced privacy control settings that are available on the social network sites.
Managing the content that the audience on the social networking sites views is essential to ensure that the privacy of the user is well maintained at all times. According to Debatin, Lovejoy, Horn, and Hughes (2009), there is a considerable difference between privacy concerns and privacy behaviors. For example, online community users, such as Facebook users, believe that other users are more at risk in comparison to themselves as they are more prone to privacy related outcomes. An analysis of research performed with the intent of establishing the relationship between privacy concerns and actual behavior on social networking sites found that those who had privacy violations performed against them were left feeling vulnerable and susceptible (Debatin et al., 2009)
Acquisti and Gross (2006) established that privacy concerns were a weak predictor of social network site use and that, for those who had joined a social networking site, there were no differences in their chances to disclose personal information. Acquisti and Gross’ study was aimed at users who had shown high levels of concern towards their privacy in comparison to those who displayed a weaker degree of interest. A study by Tufecki (2008), also established that it was the same case with those who had a weak attitude and those who had a strong attitude towards their privacy levels. The Tufecki study also identified that users, mainly those still in school, had adopted another strategy: providing their nicknames instead of their real names and adjusting the way that they were viewed on their profiles.
However, in the past, not many users were aware of the privacy settings that they could access, which is different today as more and more users are aware of the features of the privacy settings. The focus must be turned towards enhancing knowledge about privacy settings that users can access and change as they see fit. Users who had joined Facebook as soon as it rolled out were not aware of the privacy feature, although even then they were still concerned about the amount of information that they were sharing with the site and their connections (Acquisti & Gross, 2006). Strater and Richter (2007), in the course of interviews they conducted, were made aware that most respondents had problems navigating around the privacy settings on Facebook. Barnes (2006) revealed that many teenage users of the Facebook application and social media site were not aware of the publicity of their information.
Stutzman and Kramer-Duffield (2010) stated that 83 percent of the respondents with whom they interacted revealed that they were using the Facebook privacy settings and 58 percent of the respondents indicated that they had made their Facebook profile accessible to only their friends. With the various degrees of privacy and accessibility to private information on Facebook, many discussions about privacy concerns and the usage of social media sites have arisen. There were concerns about the newsfeed page that was introduced on Facebook. This page displays the activities that users engage in; for example, when they like a page or make a connection, this information is then made public to other connections on their newsfeed pages. The debate arose as, before the introduction of these pages, users could only view the activities of others only when they would log on to their friends’ pages, but now all of their activity and information was readily displayed on their connections’ news feeds. This debate left some users feeling that their privacy was threatened, and therefore resorted to monitoring what they engaged in when on the site—which was not previously a concern. Boyd (2008) stated that “Facebook participants have to consider how others might interpret their actions, knowing that any action will be broadcast to everyone with whom they consented to have a digital friendship” (p.16). Research has, therefore, revealed that there is a gap between privacy concerns and that of the actual privacy settings according to Barnes (2006). Various researchers have revealed that users of social networks provide significant amounts of personal data without having enough knowledge on how to access the privacy settings on their profiles (Lewis, Kaufman, & Christakis, 2008).
With the changes in technology, however, users are increasingly aware of the privacy settings that are available for them to utilize. With these changes, the privacy settings are also evolving, helping users become more familiar with these settings (Utz & Kramer, 2009). Users, especially teenagers, are increasingly becoming familiar with the information that they put up on the social sites and consider what they make available on cyberspace (Lenhart & Madden, 2007). In comparison,however, today’s younger generations are putting up more information that they do not consider as being necessarily personal, as compared to older generations who still consider some of the same information to be personal (Lenhart & Madden, 2007).
The teenagers’ definition of private information is not tied down to the disclosure of certain pieces of information, and they regard privacy mainly as being in control of who has access to their information (Livingstone, 2006). The theory presented by Livingstone matches definition presented by Stein and Sinha (2002) on privacy, which states, “The rights of the individuals to enjoy autonomy, to be left alone, and to determine whether and how information about one’s self is revealed to others” (p. 114).
This difficulty with distinguishing between social media privacy and privacy concerns according to Livingstone (2008) is further complicated by the fact that not many users, especially teenagers, are familiar with how to control their privacy settings. The author blames this on several factors, primarily poorly developed and designed settings on the social networking sites and the users’ limited literacy levels. These conditions make it difficult for users to properly establish those who can view the content that they put up on the social sites.
Research suggests that how users utilize the privacy settings is a concept that is influenced to a large extent by several variables, namely media coverage and privacy concerns. The commercialization of the social networking sites encourages the privacy settings to be more restrictive according to Lewis et al. (2008). The results of this research can consider a substantial improvement since the more users access these sites, the greater becomes the need to restrict the users’ information. Many sites, especially adult social sites, have made improvements to their settings, changing the default settings that are accessed by all to settings that are more restrictive.
Individuals use the Internet for exploring information, as well as making new social connections. There appears to be a shift towards most users adopting social media as a necessary tool in their lives. Organizational procedures, as well as software procedures, have invariably seen people exchanging personal information on these social media networking sites, through text messaging, instant messaging services, bulletin boards as well as online gaming sites. All these sites can invariably be seen as being social networking sites as they all support the social collaboration feature of the Internet. The term of social media refers to mainly various, loosely connected types of applications that allow users to communicate with each other as well as track discussions on the Web as they occur (Tepper, 2003).
According to Beniger (1986), mass media have replaced interpersonal communication emerging as a socializing feature that people can adopt to interact with each other. Social media networking sites, therefore, can be considered outlets where individuals, especially teenagers and young adults, can explore themselves, their relationships with each other and those that they interact with, and share their cultural backgrounds (Jenkins & Boyd, 2006).
The use of social networking sites also has a downside. The negative aspect is the result of private information being revealed on the social sites. In the United States, legislation has been proposed to control the activities of such predators such as the Deleting Online Predators Act of 2006 (“Deleting Online Predator,” 2006). In the Act, the term commercial social networking website is defined as a commercially run a website that allows users to create web pages or profiles that provide information about themselves and are available to other users; and offers a mechanism for communication between users, for example, through online forums, chat rooms, email or instant messenger (“Deleting Online Predator,” 2006).
Commercial social networking sites are increasingly being viewed as changing the existing modes of communication. The reason attributed to this is that they are capable of delivering information instantly and with urgency to their users. The community on the online social networks is viewed as largely being independent and rebellious, as they have managed to change communication behavior. For teenagers and children who have access to the Internet, there is the worry among their parents and older generations that the information that they are posting online may be too personal and revealing, posing a danger to them and their families (Sullivan, 2005). With the new communication systems, the youth is identifying ways to explore more about themselves, experiment with new behavioral systems, as well as build new friendships and relationships.
The Internet has primarily emerged as being a forum for which individuals’ behaviors are recorded on everything that they do and engage in. Using social sites, many users reveal their thoughts and ideas on the platforms. Additionally, companies and government agencies require the personal sharing information for any delivery of a service. Many individuals are not aware that their privacy has already been invaded through such actions, and therefore measures must be taken to protect the privacy of the individuals (Sullivan, 2005).
One view on information privacy is that once an individual shares certain information with another individual, the information loses its essence of privacy and consequently becomes public (Strahilevitz, 2004). This argument pertaining to information privacy is regarded as unsophisticated and unrepresentative, as stipulated by the court that sought to identify the definition as unrepresentative of a social community (Strahilevitz, 2004). Therefore, individuals consider protection of confidential information as a primary consideration for ensuring their privacy is highly maintained and respected (Picker, 2003).
According to Read (2006), trust is the ability of one to confide private information in another with the expectation of maintaining the confidentiality of the information shared by either party. Trust plays a paramount role in information sharing as it enhances the growth of new relationships and improves the development of online communication and interaction. Joining a social network, users provide highly confidential information which tends to expose a lot of personal information about the user (Boyd & Heer, 2006). Therefore, once one creates a post on their homepage, the same post is read and may be distributed to more people, thus leading to a lack of privacy of one’s data and posts. Consequently, data received from the sites is stored for future data mining activities (Read, 2006). Awad and Krishnan (2006) noted that once an individual is offline, privacy is enhanced; therefore the social sites should incorporate a system to enhance similar privacy when the user is online, through development and incorporation of privacy policies for the users’ data.
Through the use of social media, most individuals aim at developing interactions between individuals while posting current information about themselves (Coppola, Hiltz & Rotter, 2004). Profiles created seemingly for one’s friends might be detrimental to their social standing within the society, particularly in relation to political and social contexts (Coppola et al., 2004). According to Acquisti and Gross (2006), most Facebook users have provided a great deal of personal information about themselves without having an understanding of the privacy concerns attached to it. This information disclosed leads to the viewing of their profiles by strangers who consequently gain access to their personal information, thus posing a potential security threat to the individual (Dwyer, 2007).
Technology use in enhancing communication creates many privacy concerns for the user as the information incorporated remains stored within the system for long periods of time, thus rendering privacy management over the data input complex and hard to manage. Facebook, Twitter, and MySpace are the most used social networks; of these three sites, MySpace has the least trust and privacy due to increased use of the site by sexual predators (Lampe et al., 2007). According to Awad and Krishnan (2006), most social network users understand the importance of privacy on information provided but fail to incorporate measures that aim at safeguarding the information.
According to a survey comparing 68 Facebook users to 48 MySpace users, the Facebook users opted to use Facebook instead of MySpace as they consider the site to be more trustworthy about protection of information provided (Dwyer, Hiltz & Passerini, 2007). 84 percent of Facebook users were considered students, thus possibly using the site for academic purposes, whereas 65 percent of MySpace users were identified as using the site mostly for social purposes such as networking, socializing and sharing of music (Awad & Krishnan, 2006). However, the small sample of participants included in this survey limits the ability to generalize these results to the population at large. A larger sample should be used for future studies. The majority of users of the social networks are identified as highly active, as 82 percent of Facebook users continually update their status every hour (Dwyer et al., 2007). Additionally, all social site users identified that they all exceeded the neutral point of a privacy concern as the neutral point was set at 4 and a maximum of seven, and all respondents exceeded 4 to stand at 6. Facebook has been realized as having 100 percent of its users using their real names, with 94 percent including their email addresses identifying the trust placed on the site. In comparison, only 66 percent of MySpace users use their real names, and 40 percent include their email addresses (Madden, 2012). Further research has confirmed that Facebook users provide more information in comparison to MySpace users (Dwyer et al., 2007). Consequently, Facebook users use the site to manage relationships developed both on and offsite. From the above, it is evident that relationships between users develop within the social networking sites whether security and privacy concerns are made a priority or not (Lampe et al., 2007).
Online social sites vary regarding information provided by their users on the site. For instance, Facebook encourages users to use their real names as it is incorporated in university websites, thus enhancing access to learning material (Acquisti & Gross, 2006)). Friendster, on the other hand, filters user names to provide security and anonymity of the user within site, thus protecting their identity within the dating site (Boyd, 2004). Various sites include provisions for one’s hobbies, as well as educational and work backgrounds, allowing people to create relationships based on mutual interests (Facebook, 2013). Furthermore, information which is visible to other users varies based on the website. Facebook has privacy options which restrict access to the user’s private information to a select few, as designated by the user in their privacy settings (Haas, 2006). These privacy settings help to ensure the privacy of information provided by the user, as not all information is open to public viewing, thus reducing the chance of its misuse by malicious individuals prowling these sites (Schrobsdorff, 2010).
Facebook users are identified as having the largest number of users providing information about themselves: 90.8 percent of users provide a real photo, 87.8 percent provide their date of birth, 39.9 percent providing mobile phone numbers and 50.8 percent identifying their residential location (Lewis, 2009). Provision of such information renders the users vulnerable to a variety of risks which include stalking:once a user provides the location of their residence and where they work or go to school, one may use this information to identify their patterns and consequently stalk them with much ease—providing a high-security threat (Jump, 2005). Data re-identification, the process through which one can be demographically re-identified through the incorporation of their ZIP code digits, gender, and date of birth, all of which is provided for 45.8 percent of Facebook profiles (Kumaraguru & Cranor, 2005). Re-identification may be possible by use of the images put up within the profiles, which may be used either by other people to identify individuals or for commercial purposes due to increased advancement in face recognition properties.Lampe et al., 2007). Provision of information pertaining to one’s date of birth, resident location, and cell number may be used in determining one’s social security number through the re-identification process, thus increasing the opportunities for identity theft (Read, 2006).
Provision of personal data may be used in creating digital dossiers due to the low capacity for storage of such information (Lewis, 2009). Sensitive information stored concerning one’s political views or sexual orientation may be retrieved in the future and cause great harm to their social standing.(Reed, 2006). The privacy protection policy provided by the sites may not be fully applicable to the users who may receive referral friends from other networks, thus rendering the information provided public as opposed to private (Acquisti, 2004). Through the use of a fake email address, an adversary may access the verification terminal of the emails through remote access, use of an infected machine, or access to the system network, and thus verify the application rendering their access to the site and information provided by other users (Acquisti & Gross, 2006). Consequently, through the incorporation of social engineering, access to users’ accounts is simplified, especially within Facebook which only requires a single friend request to be sent to a user to have access to their personal information upon the user’s acceptance of the friend request (Awad & Krishana, 2006). This was successfully evaluated through the use of an automatic script that sent friend requests to 250,000 different users and was accepted by 75,000, thus enhancing access to data provided by all these users to the automatic message (Jump, 2005). Also, incorporation of advanced search features has significantly compromised the privacy of user accounts on social media (Boyd & Heer, 2006). Through the development of search engines, one can be traced based on the information provided concerning schools attended, physical location, or phone number, exposing the users to greater risks related to tracking capability (Haas, 2006).
Privacy has continually become a paradigm factor among social networks and online providers. This paradigm has led to the development of privacy-enhancing technologies (Post, 2001). Technologies enhance assurance of privacy, although the occurrence of privacy breaches is still high with online sites and sources which provide access to personal information to individuals, governments, and corporations (Almeida, 2012). An example of this accessibility is shown within Facebook, as the social site has incorporated a trade-off system with marketing and internet companies and shares personal information provided by its users. This sharing of information has led to an uproar by the users, as the information was highly private and aimed to stay as such instead of being made public to other sites (Lewis, 2009). Facebook has gradually evolved the privacy functions and settings within its site to ensure that the privacy of information provided by its users is guarded against fraudsters or impostors (Kumaraguru & Cranor, 2005).
The need for continuous improvement of the privacy settings shows that development of privacy technologies is highly complex, and made even more difficult by the development of hacking systems (Lewis, 2009). Furthermore, hacking or access to private information by the public has significantly affected government institutions as identified through WikiLeaks, and most recently by the expose made on the U.S. intelligence system on online media by Edward Snowden (Almeida, 2012). This information disclosure has led to the development of the ‘do not track’ policy by the US Federal Trade Commission to ensure that manufacturers develop non-monitoring browsers to discourage external monitoring. Consequently, based on Edward Snowden’s leak on the U.S surveillance system, the U.S. government is developing measures to restrict access to the information received from the surveillance conducted on citizens around the world (Almeida, 2012). This type of event leads to the development of the questions of the legality of the surveillance conducted by governments on both its citizens and other individuals around the globe. These developments aim to protect individual privacy from such surveillance as the current tort law is no longer applicable in such instances.
Increased learning and provision of information on the importance of maintaining individual privacy has been realized to enhance protection of information provided on social sites, combined with the limiting of access to the information provided by online users on the social sites to specific groups of people, thus enhancing information privacy (Sweeny, 2002). According to Madden (2012), a recently conducted study identified that 63 percent of adults had set their online profiles to be only accessible to friends, while 19 percent has set their profiles to be partially accessible. Women have been identified to be more privacy-conscious as compared to men, with 67 percent of women restricting profile access to friends while only 48 percent of men do the same (Madden, 2012). Age had previously been a variance with privacy settings, with the younger population being less concerned about privacy, but this has changed over time with both the young (17 percent) and the old (20 percent) selecting private or semi-private privacy settings on their profiles (Madden, 2012). However, management of the privacy controls has been both easy and arduous for various individuals, with 48 percent citing the process difficult while 49 percent find it easy, with the older generation of 30-64 years finding the process more difficult as compared to people between the ages of 18-29 years (Madden, 2012). Consequently, more users are removing tags on photos (49 percent), undesired friends (63 percent) and comments (56 percent) posted on their site (Madden, 2012).
Modern open-source intelligence (OSINT) gathering, the process of collecting, processing, and analyzing individual information that has been shared on open sources and thus made available to the public, started in the mid-1990s, and over 40 countries have developed an open-source intelligence gathering command, cell, or unit (Steele, 2007). The history of open-source intelligence gathering will continue to grow given the emerging threats, and non-state actors and where conventional forces remain static. The information gained from open-source intelligence gathering tools can produce up to 80 percent of the intelligence needed on an individual or group in real time (Steele, 2007). Open-source intelligence gathering tools can be immensely beneficial to civil affairs, psychological operations, and intelligence operations.
The use of open-source intelligence gathering is not a new concept. Open-source intelligence gathering is the result of the pioneering work of the 1930s. The first organization to use open-source intelligence gathering was Princeton University, which monitored foreign short-wave radio waves. This monitoring was eventually overtaken by the Foreign Broadcast Intelligence Service (FBIS) in 1941. The FBIS used radio waves as a central intelligence source during World War II. During this time, the United States government did not overlook printed sources. This task was left to the Interdepartmental Committee for the Acquisition of Foreign Periodicals (IDC) to gather information through printed publications (Mercado, 2004).
At the completion of the Second World War, the use of open-source intelligence gathering increased at the start of the Cold War. The FBIS and the Foreign Document Division (FDD) in conjunction with the Central Intelligence Agency (CIA) uncovered a link between Moscow and Beijing. CIA covert officers contested this work which lasted long into the next decade (Ford, 1998) and (Ford, 1997). This uncertainty did not cause OSINT to avoid the benefits of the intelligence-gathering arena. During the cold war, OSINT provided a considerable part of intelligence on the Soviet Union, China, and others. This information was gathered from printed documents from those nations (Bagnall, 1958). A Russian insider asserted that OSINT produced a significant amount of military intelligence (Moore, 1963).
The continuation of OSINT in the intelligence community will not reduce the use of social media sites, and the internet will only increase the value of OSINT. The collection of this open-source information is the building block of secret intelligence. This increase in technology will not help against countries such as North Korea and others (Hulnick, 1999). This technique is called social media intelligence (SOCMINT) and should be considered a branch of open-source intelligence (OSINT) (Schaurer, 2012). One of the earliest forms of SOCMINT was social network analysis (Franke & Rosell, 2016; Hui, 2016).
Social network analysis relies on computer-mediated communication (CMC) (Garton, Haythornthwaite, & Wellman, 1997). Social network analysis examines how individual users interface with their computers, and how individuals and small groups function online (Garton et al., 1997). Flulk and Steinfield, (1990) and Wellman et al. (1996), social network analysis, should not examine just the single user but should examine the individual group as a whole. Additionally, Wellman and Gulia (1997) asserted that virtual communities should also be examined. These ideas were proposed by these authors before the social media revolution. Social network analysis was the first open-source gathering tool used in computer-mediated communications. This analysis laid the groundwork for other open-source gathering tools (Miller, 2012).
Berkowitz (1982), Wellman et al. (1996), and Wasserman and Faust (1994) stated that social network analysis determines the relationships of individuals, traces the flow of information and examines the behavior and attitudes of the individuals. There are four units of analysis associated with social network analysis, which are relations, ties, multiplexity, and composition (Garton et al., 1997). The connection can be characterized by content, direction, and strength. The content refers to the resource of the exchange. When dealing with a CMC, the relations include personal matters and private information (Garton et al., 1997). According to Marsden and Campbell (1984) and Wellman and Wortley (1990), these relations differ in intensity. The relations can include discussion of contrary information, emotional support, vague or equivocal communication, communication to generate ideas and a consensus, foster amicable relations, and support a virtual community (Daft & Lengel, 1984; Fish, Kraut, Root, & Rice, 1992; Garton & Wellman, 1995 ; Haythornthwaite, Wellman, & Mantei, 1995; Haythornthwaite & Wellman, 1996; McGrath, 1984, 1990, 1999; Kiesler & Sproull, 1992; Rice & Love, 1987; Van de Ven, Delbecq, & Koening, 1976; Wellman & Gulia, 1997).
A tie connects two or more individuals by one or more relationships (Garton et al., 1997). According to Marsden and Campbell (1984), ties can be categorized into two groups, weak and vigorous. A soft link is infrequently maintained non-intimate connections. A strong tie includes intimacy, self-disclosure, and provision of reciprocal services, frequent contact, and kinship (Garton, et al., 1997). These strong ties are what exist currently on online social networking sites. These ties are what make open-source intelligence gathering tools so helpful in gathering this information. The weak ties can also provide access to more diverse resources. The reason that the resources can provide diverse resources is that the individual is connected to different social networks and resources.
Multiplicity is a direct result of relations and ties (Garton et al., 1997). The multiplicity ties are more intimate, voluntary, supportive, and durable (Wellman & Worley, 1990). The composition is a combination of the social attributes of the participating individuals (Garton et al., 1997). The composition of CMC should focus on the content of the message rather than the attributes of senders and receivers (Garton et al., 1997). Social network analysis is the foundation of using open-source intelligence gathering tools and social media to compromise privacy.
The use of social network analysis and open-source intelligence has evolved into SOCMINT. According to Bartlett and Miller (2013), SOCMINT can utilize six different detections, processing, and analyses: Natural language processing, Event detection, Data mining and predictive analytics, Social network analysis, Manual analysis, and Solicited. Natural language processing is a form of artificial intelligence and incorporates machine learning and provides a computational analysis to natural language found in many social media networking sites (Magesh & Nimala, 2016). The event detection of SOCMINT uses statistical detection of social media networking sites’ content to detect events in a dynamic environment (Reuter, & Cimiano, 2012; Thelwall, Buckley, & Paltoglou, 2011; Marcus et al., 2011). Data mining and predictive analytics utilize statistical analysis against ‘big data’ datasets to determine causal relationships between connections (McCue, 2014; Larose, 2014). Next, the social network analysis applies mathematical techniques to discover topography of connections from information gathered from social networking sites (del Fresno García, Daly, & Segado Sánchez-Cabezudo, 2016; Suvilehto, Glerean, Dunbar, Hari, & Nummenmaa, 2015; Lewis, Kaufman, Gonzalez, Wimmer, & Christakis, 2008). The human aspect of SOCMINT is manual analysis. The manual analysis utilizes netnography techniques of observations in anthropology to study social networking site use interactions and experiences (Kozinets, 1998). Additionally, this manual analysis utilizes a qualitative method which discovers contingent social significances (Lassnig, Markus, Eckhoff, & Parson, 2016; Kaplan, & Haenlein, 2010). Lastly, solicited method is associated with human intelligence where individuals ask social network site users for information (Franke, & Rosell, 2016). Using the well-known methods above, the collection of social media messages can reflect the trends of individual dynamics (Aslam et al., 2014); (Nagel et al., 2013).
The following sections incorporate a political, economic, social, technological, and legal (PESTL) analytical technique to investigate the macro environmental impacts of intelligence gathering technology (IGT)s’ ability to exploit the privacy of users on social networking sites potentially. The use of online social media networks, to generate political outcomes, has been made exceedingly clear by the uprising in Egypt, Tunisia, Syria, and Libya (Safranek, 2012). The 2012 presidential election campaign used social media networks to reach the young voters in the United States. Both the Presidential election and the Arab Spring used social media to lower the socio-economic barriers commonly associated with access to information (Safranek, 2012). The online social media sites allow individuals to create and form activities of civil society groups. These formations are in the form of mobs, movements, and civil society organizations. The use of online social media facilitates their creation (Etling, Robert, & Palfrey, 2012).
The first time social media was used to create political unrest was in 2002, in the Philippines. The political turmoil was the result of the impeachment trial of the Philippines president when the Congress voted to acquit evidence against the president. As a result, the citizens of the Manila used social media and text messages to stage several day-long protests over the evidence. This protest caused the Congress to reverse course and allow the evidence to be presented in the impeachment hearing (Shirky, 2011).
The next event that used social media networks to create political unrest was in Moldova, 2009. According to Hodge (2009), this was the first widely recognized use of social media networks to promote political turmoil. During this uprising, the citizens of Moldova used Facebook, LiveJournal, and Twitter to organize a protest within the former Soviet republic. According to Amin (2010), this protest was not successful in bringing about political changes in Moldova; however, the movement did bring awareness to digital activism through online social media sites.
The next country to experience political unrest as a result of social media was Tunisia. In early 2011, the citizens of Tunisia ousted the former president. Tunisia has the most the oppressive Internet censorship outside the Gulf countries. In Tunisia, 33 percent of the population is online, 16 percent use Facebook, and 18 percent use Twitter (“Middle East: Social media,” 2011). Rash (2011) asserts that social networking sites did help with the coordination and dissemination of the protesters; however, the use of these new technologies was not the reason for the overthrowing of the government.
Egypt was the next country to fall victim to social media being used to cause political unrest. This uprising was the result of the beating of an Egyptian business person for the confiscation of his marijuana. This beating was caught on film and was uploaded to YouTube, and the activist created a Facebook page. This Facebook page received approximately 500,000 members (“Information age: Egypt’s,” 2011). The creation of Facebook led to the citizens of Egypt to protest in Cairo’s Tahrir Square. In response to the protest, the Egyptian government blocked access to social media sites and restricted Internet access. Help from the international community to allow users to use social media for organization and communication (Safranek, 2012) subverted these actions by the Egyptian government. Because of this help, the tweets from Egypt increased from 2,300 a day to 230, 000 a day. In addition to the tweets, the number of videos showing the occupation and political commentary went viral. The top 23 viral videos, regarding the protest and political commentary, received 5.5 million views. The information that was posted on social networking sites also increased dramatically (O’Donnell, 2011).
As the Arab Spring spread, the use of social media did continue to be used; however, the lack of technology infrastructure in Lebanon, Libya, and Yemen was not as affected as in Tunisia and Egypt (Naim, 2011). According to Riley (2011), there is not an active online activism in Lebanon, Libya, and Yemen. In Lebanon, a Facebook page only received around 15,000 followers (Naim, 2011). This lack of online activism is a substantial difference from the 500,000 of the Egypt page on Facebook (O’Donnell, 2011).
In the United States, we have been spared the political unrest associated with online social media activism; however, in 2012 the presidential campaign both candidates used social media. During the campaign, President Obama had 28,658,765 Facebook fans and 19,806,314 Twitter followers. The challenger Mitt Romney had a total of 6,961,665 fans on Facebook and 1,123,637 Twitter followers (Knight, 2012). Gladwell (2010) asserted that social media platforms are built on weak ties between individuals and that weak ties cause them to participate less. Also, Gladwell argues that because of these weak links online social media is not conducive to sustain high-risk behavior associated with the social change in the 1960’s civil rights movement. According to Christensen (2011), two political theories address the use of social media. The first premise is techno-utopians. Individuals who believe in this theory usually argue that society often relies too heavily on new technologies. Also, these individuals diminish the effects of economics, gender, and other material conditions. The other theory is techno-dystopian. Individuals who follow this theory typically insist that technology does not represent a significant role in society and that political communications can be seismic in nature.
The early analysis of how social networks affect the economies were the works of Myers and Shultz (1951), and Rees and Shultz (1970), who showed how individuals obtained jobs through social networks. These works laid the foundation for Granovetter (1973, 1995). Granovetter (1973) demonstrated that the degree of overlap is proportional to the relationship of individuals in social network sites with strong ties to one another. The study showed the principle of the relationships is comparable to the diffusion and influence of information, mobility opportunity, and community organization. Building on Granovetter (1973), Boorman (1975) and Montgomery (1991) revealed that individuals in social networks make explicit choices about ties that affect their employment and wages. The online social networking sites have now been used to seek and obtain employment by ones and zeros instead of word of mouth. This digital dissemination of job offers and job requests has a direct effect on the economy. This shift in the paradigm has transformed the way conduct job searches and marketing. Online social media also has a direct impact on the economy through online marketing which is called word-of-mouth and is profoundly impactful. According to Misner (1999), word-of-mouth is a highly effective marketing tool. According to Trusov, Bucklin, and Pauwels (2009), the online social media sites provide users with a very user-friendly way to use word-of-mouth marketing. Furthermore, online social media sites offer several different variants of word-of-mouth. These modifications include viral marketing, referral programs, and community marketing. An essential factor for this marketing to work is valorization of surveillance (Cohen, 2008).
According to Mosco (1996), this valorization of surveillance uses extensive commoditization to market and reshape individual’s lives. This commoditization allows marketers to enter spaces that had been untouched by capitalist social relations. The monitoring is used to gather information for other companies to provide demographics for marketing purposes. This surveillance is also used to retain a member and keep them returning to the site (Cohen, 2008).
Online social media are affecting the economy in a positive way. Social media has evolved from the days of gaining employment via social circle to now being able to reach individuals worldwide. In addition to helping individual’s find employment, social media is allowing consumers to share information via word-of-mouth and provides a free form of marketing. The information collected from social media is providing revenue for businesses.
Social media have an effect on the social aspects of society. According to Wellman et al. (1996), social media sites are used to maintain existing relationships with individuals and to establish new relationships with individuals. Additionally, early research on social media assumed that individuals would interact with individuals outside of their geographical location and from their pre-existing social groups. The works of Parks and Floyd (1996) further support this by revealing that one-third online social media connections lead to a face-to-face meeting.
This face-to-face meeting results in what is called social capital. According to Coleman (1988), social capital is the accumulation of resources from the relationships of individuals. Furthermore, Bourdieu and Wacquant (1992) asserted that social capital is the sum of both actual and virtual resources that result in a network of institutionalized relationships. According to Adler and Kwon (2002), social capital relationships have been associated with several beneficial social outcomes.
These positive results include improving public health, lower crime rates, and more efficient financial markets (Adler & Kwon, 2002). An example of how social capital and social media can positively influence public health can be seen in the pandemic of the H1N1 virus. The Centers for Disease Control and Prevention (CDC) used social media and the relationships of social capital to distribute information about the virus to the population (“CDC to tap,” 2009). An example of how social capital and social media can positively reduce crime rates can be seen in Chicago. The mayor of Chicago is using a social media based platform to observe the retaliation between members of various gangs. The mayor hopes that this new platform will be able to identify potential targets of gang violence (Rosenbush, 2012). Helliwell and Putnam (2004) asserted that social capital could be used for harmful purposes; however, social capital does have a positive effect on individuals in a social network.
According to Granovetter (1973), social capital outside an individual’s close network offers non-redundant information. This non-redundant information can lead to benefits in one’s social status. Additionally, Paxton (1999) asserted that individuals’ social capital is gained by individuals’ desire to draw on resources from the network. These resources can contain information, relationships, and other organization. Ellison, Steinfield, and Lampe (2009) stated that only particular types of social media influence social capital levels. The social media that has the most influence is Facebook. Facebook allows an individual to publish both significant life changes and ephemeral activities. This publication of information allows individuals to participate in frivolous social surveillance (Lampe, 2009).
The use of social media has caused a generation to stop seeking opportunities to engage other individuals in pleasantries that are usually common in public spaces (Lampe, 2009). Conversely, the lack of exchanging pleasantries in public places because of social media is not necessarily a negative. The visible accouterments posted on social media can provide signals that allow individuals safe access to topics of conversation. Also, social media can be used for individuals to connect with individuals from their community and lowers the barriers for interactions (Ellison et al., 2009).
Social media uses Web 2.0 technology which allows for information that once was not readily available or discoverable to be manipulated in a way that is not the intended use of the data (Milier, 2005). Dale Dougherty created the catchphrase ‘Web 2.0’ in 2004 (O’Reilly, 2007). According to Anderson (2007), there are six key areas of how Web 2.0 affects social media including (1) Individual production and user-generated content, (2) Harnessing the power of the crowd, (3) Data on an epic scale, (4) Architecture of participation, (5) Network effects, and (6) Openness.
The individual production and user-generated content have led to the widespread use of social media. Web 2.0 technologies allow individuals from around the world to share information and other digital media, such as photographs and videos and allow the users to tag these items with a tag. These tags allow any user with Internet access to search the items with a few simple mouse clicks. Additionally, Web 2.0 technologies have lowered the entry barrier for non-tech savvy users. The use of Web 2.0 has given the everyday user the catalyst for self-expression (Downes, 2004). The revolution of Web 2.0 technologies can be compared to the self-publishing revolution of the 1980’s, which was induced by laser printers and desktop publishing software (Hetzfeld, 2005). This production is very beneficial in a society where being noticed is everything (Anderson, 2006). This individual production and generated content allow intelligence-gathering tools to collect personal data and information on the individual who produced the data (Anderson, 2006).
The use of the Web 2.0 and harnessing the power of the crowd has been seen in the 2011 and 2012 Arab Spring revolution. Surowiecki (2004) asserts that harnessing the power of the crowd via the Web 2.0 can solve three different types of problems which include cognition, co-ordination, and co-operation. The solving of these three problems has created a new issue with knowledge and intelligence gathering tools, which gather the information used to explain the cognition, co-ordination, and co-operation (Surowiecki, 2004).
The use of the Web 2.0 and social media has created data on an epic scale. Web 2.0 is best described by the words of Von Baeyer (2003): “Information gently but relentlessly drizzles down on us in an invisible, impalpable electric rain” (p. 3). The hidden and private data representing the rain which Von Baeyer referenced is collected by intelligence gathering tools. This information can then be used to compromise the privacy of that individual (Von Baeyer, 2003).
The architecture of participation is associated with the Web 2.0 and social media, which expands on and builds collaboration and user production. This architecture style is what allows for easy entry into the use of the Web 2.0 technologies. At the advanced level, the architecture improves itself with the general use of the Web 2.0 technologies (Anderson, 2007). The openness associated with the Web 2.0 is very influential on intelligence gathering tools. This openness of the Web 2.0 is what allows the intelligence tools to work.
According to Anderson (2007), the Web 2.0 operates on two key concepts that affect the network effect. The first concept referred to the size of the social media network and the economic and social implications of adding new users to the network. The second concept refers to the power law and the Long Tail phenomenon. Also, the use of the web program language has increased use of the Web 2.0 technologies. One of the most popular features was the unrestricted use of custom Hypertext Markup Language (HTML) code (Boyd & Ellison, 2008). The allowed use of HTML code on social media sites such as MySpace created a population of teenagers that learned how to copy and paste HTML code from various sources, creating personalized profile backgrounds and layouts (Boyd & Ellison, 2008). According to Liebowitz and Margolis (1994), and Klemperer (2006), the Network Effect is referred to as an increase in value to the existing network. This value is the result of interactions with other users within the network. An example of the Network Effect is illustrated by the launching of both MySpace and Facebook. As more and more people started using these social media sites, the Network Effect grew because more people could easily access others to communicate with them, thus improving their social media experience. (Anderson, 2007).
This network effect was originally described by what became known as Metcalfe’s Law. This heuristic was created by Robert Metcalfe, the inventor of the Ethernet. The law asserts that the value of a network is proportional to the square of the number of nodes (Anderson, 2007). However, according to Briscoe, Odlyzko, and Tilly (2006), Metcalfe’s law is incorrect, and the value of a network of size n grows in proportion to n (log)n2. This description provides a more actual reality of the scale of the network, instead of the larger size of Metcalfe’s Law (Briscoe et al., 2006). On the contrary, Metcalfe’s Law is one of the defining characteristics of the information revolution or paradigm associated with social media (Castells, 2000).
Several technology applications are associated with the web evolution referred to as Web 2.0. The most notable application, which is used by intelligence gathering tools, is open APIs. These tools use these APIs to scan the social media sites for information. There will be a brief discussion of applications with in-depth discussions of open APIs. The other major applications are AJAX, alternatives to AJAX, SOAP, and REST, and Microformats. Applications such as AJAX increase interactivity between various web applications, thus allowing users to utilize them in conjunction for better productivity (Johnson, 2005).
The lightweight technology, which is also utilized in the programming of the web 2.0 technology, is Simple Object Access Protocol (SOAP) and Representational State Transfer (REST). These simplified programming models include the scripting languages such as Perl, Python, PHP, and Ruby. Also, these technologies also use RSS, Atom, and JSON (Anderson, 2007). REST stands for Representational State Transfer, which was introduced by Roy Fielding (Costello & Kehoe, 2005). REST is not a standard, but it is an approach to communications interface that uses XML and HTTP. SOAP is more of the verb-noun system and allows the creations of irregular verbs (McGrath, 2006).
Microformats are also used in the web 2.0 technology. Web developers embed semi-structured semantic information through the use of microformats (Khare, 2006). An example of microformats is the vCard used to organize contact information on the website. The microformats will allow bloggers and website owners to embed information, services, and applications, so the end user does not have to leave the website. Microformats allow intelligence-gathering tools to gather information automatically from the site (Anderson, 2007).
The main Web 2.0 technology used in association with intelligence gathering tools is open APIs. API stands for application programming interface. The use of APIs allows a mechanism to be programmed to access data without accessing the source code. These open APIs have allowed for the rapid growth of the Web 2.0. Several APIs are used in the utilization of open-source intelligence gathering tools, and for this reason, their functionality is important to our current research. A brief description of the Facebook open graph API, Google custom search API, Twitter API, and Google+ API follows.
The first API listed is the Facebook Open Graph API. In 2010, this API was unveiled to allow social engineers to complete a quick search of user’s profiles, posts, events, groups, and allows for searches on keywords within the user’s profiles. The second API is a Google Custom Search API. This API allows the open-source intelligence-gathering tool to have a custom search engine (CSE). This custom search engine can be used to search all social media sites for keywords or users of the sites. As an example, the social engineer can create a CSE that would scan LinkedIn user’s profiles for any keywords to search education and job title. Also, this CSE can be used to find Twitter profiles.
The third API is the Twitter API. This API allows a social engineer to retrieve extensive information for up to 100 users. The information that can be extracted includes Twitter handles, name, display profile information, profile description, links to profile image, geolocation enabled on Tweets, and profile description. Also, this API can also provide the followers and friends list of a user that was searched. The fourth API is the Google+ API. This API allows the social engineer to enumerate information from Google+ users. This API does not have the ability to retrieve the circle information for a profile; however, this can be achieved by a custom API (Anderson, 2007).
When collecting data from social media platforms, an individual could manually collect the data. This manual collection could be completed by copying, screen grabbing, note-taking and saving Web pages (Bartlett & Miller, 2013). These manual collection methods are not feasible for large amounts of data. The collection of large quantities of data is best collected by a connection to the social media platform through an API. These APIs deliver historical information and information regarding the user, the followers, and the profile (Bartlett & Miller, 2013). Bartlett and Miller (2013) noted there were seven types of API that had access to Facebook data. The Facebook Graph API can collect posted text, events, or URLs, comments, and metadata on user information to include gender and location. Bartlett and Miller noted Twitter has three different APIs that can be used for intelligence gathering. These three distinct APIs allow for different percent of tweets to be collected. The three different types of feeds include public, white-listed research account, and commercial. The public feed will allow the user to collect one percent of the total daily number of tweets. The white-listed research account feed will allow the user to collect 10 percent of the total daily number of tweets. The commercial account will allow the user to collect 100 percent of all tweets. Fodden (2011) revealed that each API produces 33 pieces of meta-data. The meta-data can show the geolocation in a longitude and latitude coordinates their time-zone, some tweets, and others. Web scrapers and crawlers are also used in the collection of open-source social media intelligence gathering. Bartlett and Miller (2013) described scrapers and crawlers as an automated program that catalog information located on websites.
Privacy, although commonly discussed, does not have one single definition. The definition and value of privacy differ from culture to culture. In philosophy, Aristotle’s definition of privacy separates the public sphere of political activity from the private sphere of domestic life. In American law, privacy treatises started appearing in the 1890’s, and privacy was protected mainly on moral grounds (Stanford Encyclopedia of Philosophy, 2002).The physical space can be either a private or public space. When society classifies a physical space as private, a privilege is placed upon the physical space. This privilege establishes relationships that make the physical space private (Alfino & Mayes, 2003). For example, a user on the social media website Facebook can set his or her Facebook page to private, thus theoretically making the page private. The events of September 11, 2001, changed the views of many Americans, who accepted the fact that privacy is not as important as national security.
According to Maslow (1950), self-actualized individuals require privacy and view it as a basic human need. Privacy is supremely essential to most individuals; however, individuals seem to overlook privacy concerns while on social media. This disregard of privacy is supported by the work of Debatin, Lovejoy, Horn, and Hughes (2009), which concluded that users were not concerned about privacy while on Facebook. This lack of concern could be the result of individuals using the traditional definitions of privacy. Scott (2008) and Sykes (1999), claimed that the traditional definitions of privacy are obsolete and that individual privacy does not exist. This traditional view of privacy is further supported by Chen and Shi (2009) which attributes the loss of individual privacy to the development of internet technology and social media. Solove (2010) asserts that the definition of privacy should be updated to reflect the proliferation of modern technology.
The United States Constitution does not give citizens the right to privacy; however, some legal scholars would argue that the Fourth Amendment would imply a right to privacy, although it does not contain the word privacy specifically (Gomberg, 2012). The Fourth Amendment protects citizens from unreasonable searches and seizures. Also, privacy protection is needed from federal and state governments, as well as other entities and individuals. This protection is provided through the Privacy Act of 1974 and the revised Act of 1980. Furthermore, the Electronic Communications Privacy Act of 1986 provides individuals with protections from unlawful interception of electronic communications. Also, the Stored Wire and Electronic Communication and Transactional Access Act provide individuals with the privacy of communications that are stored, such as e-mails, profiles, and other databases. According to Gomberg (2012), the Stored Wire and Electronic Communication and Transactional Access Act has caused controversy and has been used in the invasion of privacy litigation involving social media. Lastly, Brandies’ dissent from Olmstead v. U.S. 1928 asserted, “… To protect that right, every unjustifiable intrusion by the government upon the privacy of the individual, whatever the means employed, must be deemed a violation of the Fourth Amendment. (p. 277)” This dissent implies the right to privacy is an intangible concept and can be invaded by subtle means, despite being protected by legislation such as the Fourth Amendment and the Stored Wire and Electronic Communication and Transactional Access Act. These subtle means can be applied to open-source intelligence gathering tools and social media.
The other side of privacy and social media deals with national security. On the morning of September 12, 2001, American perspective on privacy changed. The legislation which followed the attacks changed the concept of privacy for citizens of the United States. The main legislation that affects privacy was the Patriot Act (H.R. 3162). The Patriot Act granted the United States government agencies an unthinkable license to monitor the private lives of the United States citizens. The Patriot Act allows government agencies to monitor various communications in non-emergency situations electronically. History has shown us that electronic monitoring if left unchecked, can be very dangerous (Henderson, 2002).
The danger of unchecked surveillance was a concern of the 2007 United States Judiciary Committee hearing on balancing privacy and security, with a focus on the privacy implications of government data mining programs. The committee asserted that during the Bush administration, the use of government data mining programs dramatically increased. The data that was being collected was sensitive personal data. This data mining was conducted with microscopic congressional oversight and safeguards (Casman, 2011).
Another key piece of legislation that deals with privacy and information on social media and the Internet is the Foreign Intelligence Surveillance Act (FISA). This act is highly secretive in nature. The information that is available about the Foreign Intelligence Surveillance Act is the information that law enforcement agencies disclose to the public. This lack of disclosure causes the public to be unaware of the extent of government agencies’ access to records containing personal information from various organizations (Jaeger, John, & Charles, 2003). FISA has protection built to ensure the inalienable rights guaranteed to Americans and permanent residents. FISA asserts that no United States citizen can be considered a foreign agent or power based on his or her rhetoric; this right is protected by the First Amendment even if the individual Fourth Amendment rights were violated to obtain the information (Carroll, 2006).
In 2008, FISA was amended. This amendment removed the requirement that the government obtains a warrant from a special court when conducting electronic surveillance of citizens abroad. This amendment now allows the United States government to monitor large groups with one request. Also, the Foreign Intelligence Court, which governs the FISA application, now has slight oversight (Totenberge, 2013). The constitutionality of the FISA was ruled on by the United States Supreme Court in the spring of 2013. The court asserted, in a 5-4 vote, that “human-rights advocates, journalists, and lawyers for detainees could not show with near certainty that they had been or will be harmed by the program and, therefore, they could not challenge the statute in court” (Totenberge, 2013).
Privacy is connected with several different areas of American law. These areas include the Fourth Amendment, trade secrets, patents, evidence, the constitutional right of information privacy, and the Freedom of Information Act (Strhilevitz, 2004). The right to privacy is defined by each state. In Georgia, an individual can disseminate private information to dozens or thousands of individuals, without making that information public; however, if the same individual was in Ohio and disseminated information to three individuals, that information would then be considered public. The advancements in technology, social media, and open-source intelligence gathering tools have the capabilities to spread that once-private information globally.
Using SOCMINT in law enforcement may be difficult to carry out within a legal framework. The reason for this difficulty is that the collection of any information may require legal authorization. The framework which would be used to adhere to this decision would be the reasonable expectation of privacy framework (Bartlett & Miller, 2013). According to Brenner and Frederiksen (2002), there are two specific areas to use framing a reasonable expectation of privacy for the United States law enforcement. The two are the Fourth Amendment and Katz v. the US, 389 US 347 (1967). Brenner and Frederiksen, based on these two legal documents, argue that a reasonable expectation of privacy is met if two conditions are met. The first condition is that an individual must believe the information is private; the second condition is that the society the individual lives in must also believe the information is private. Additionally, these two conditions were re-affirmed in US v. Jones, 132 S. Ct. 945, 565 U.S. 945, 181 L. Ed. 2d 911 (2012). In Jones, the United States government placed a global posting device on the defendant’s vehicle and tracked the defendant’s actions over the course of four weeks. Justice Sotomayor articulated that the government use of the GPS unit to conduct surveillance was indeed an invasion of the privacy of the accused.
Consequently, through the joining of self-help groups such as Alcoholics Anonymous (AA), individuals share most of their secrets and struggles about their situations with a particular group. This sharing of information involves highly sensitive and private information that should not be disclosed to the entire public, as through sharing the individuals gain psychological relief, whereas if this information were published to the entire world, they would undergo psychological distress and consequent depression (Lichtman & Posner, 2002). In the disclosure of private information to friends, one relies on their goodwill, confidentiality, or results of adverse consequences upon disclosure to ensure the information remains private as such is not covered by the tort law (Aviram, 2003). However, the law protects against any form of private information once relayed to the public that causes excessive harm and damage to the individual through increased public exposure thus warranting legal liability (Aviram, 2003). Consequently, leaked information involving a relationship that may cause harm to either of the individuals’ economic status is also protected by the law. Therefore, invasion of privacy in various elements incurs legal liability with the media being identified as the defendant and the victim as the plaintiff (Picker, 2004). Consequently, incorporation of tort law protects individuals from privacy information when the information shared in confidence within a social setting is made public while it was private.
Tort law relating to public disclosure of private information aims to ensure that disclosure of private information is used and regulated to ensure that it provides more social benefits than harm (Strahilevitz, 2004). The law encourages the sharing of information that will develop stronger interpersonal relationships and provide psychological peace and relief derived from the sharing process (Picker, 2003). The tort for publicizing private information extends limited liability to culprits involved in rendering private information public that is not of concern to the public and is consequently related in an offensive manner (Cooter & Porat, 2004). This law helps avoid submission of intangible cases to court and protects First Amendment right of individuals by providing them immunity; consequently, the law helps protect disclosure of information whereby if it was under mutual consent no law is applicable, but if it was not subject to open disclosure, the courts argue that confidentiality of the information must be upheld (Picker, 2004).
The law helps protect the spread of information that promotes the development of innate relationships as opposed to already developed and identified information between either party (Aviram, 2003). The parties’ expectations on maintaining the privacy of the information shared may vary, as some will regard the information as private and not be disclosed, while the defendant may have regarded the information public and hence disclosed it. This information disclosed then leads to incorporation of subjective inquiry into the publication of the information based on the expectations of either party upon dissemination (Cooter & Porat, 2004). In solving breach of privacy cases, courts incorporate the computer model as opposed to public opinion. Consequently, through the incorporation of the Fourth Amendment, public opinion would help in identifying the privacy breach in question. The limited privacy law states that once an individual discloses or confides private information to one or more individuals, the person expects that information to be treated with the utmost confidentiality and not be divulged or made public under any circumstance as identified in Sanders v. ABC, 978 P.2d 67, 85 Cal. Rptr. 2d 909, 20 Cal. 4th 907 (1999).
Chapter 2 included a comprehensive literature review which discovered a gap in the existing literature regarding IGTs and the potential to compromise privacy by gathering personal information from social networking sites. Additionally, within the literature review, the (PESTL) analysis addressed the macro environmental impacts of the ability of IGTs to potentially exploit the privacy of users on social networking sites. Chapter 3 will provide the methodology that this study will use to collect and analyze college user attitudes and behaviors regarding OSINT gathering tools and the potential to compromise privacy through social media. The study will follow an empirical methodology which will consist of a voluntary web-based survey.
This literature review has introduced conceptual research and illustrated the gap in relevant research regarding OSINT gathering tools and social media privacy. Studies reviewed in this literature review made significant contributions to the field of social media privacy in several areas. These areas included privacy concerns regarding the information posted on social networking sites (Strahilevitz, 2004). Additionally, Gross and Acquisti, (2006a) and Barnes (2006), addressed concerns regarding the negative ramifications of information posted on social networking sites. Furthermore, Finder (2006), Hass (2006), Santora (2007), Quan-Haase (2007), and Ellison, Steinfield, and Lampe (2007) noted a lack of privacy concerns among college students regarding social networking sites. Lastly, the studies conducted by Dwyer, Hiltz and Passerini (2007), Lewis, Kaufman, and Christakis (2008), Lewis et al. (2008), Debatin, Lovejoy, Horn, and Hughes (2009), Lewis (2009), Krasnova, Spiekermann, Koroleva, and Hildebrand (2010), Lindqvist, Cranshaw, Wiese, Hong, and Zimmerman (2011), Hannay and Baatard (2011), Madden (2012), and Almeida (2012) addressed social network sites privacy attitudes and behaviors of users. The (PESTL) analytical technique investigated the macro environmental impacts of IGTs’ ability to potentially exploit the privacy of users on social networking sites. Given the high level of privacy risk presented by OSINT gathering tools and social media privacy, a closer examination of college user attitudes and behaviors regarding OSINT gathering tools and the potential to compromise privacy through social media are both timely and greatly needed. This study will make a contribution to the field of IGT’s and the potential to compromise privacy through social networking sites and address the gap in the current literature. It will differ from previous research conducted on the subject because it will measure the varying degrees of privacy concern by various user demographics—specifically gender, grade level, and age. Additionally, the data will be collected through an online survey which will allow participants to complete the survey from the comfort of their homes, thus increasing the likelihood of participation.
The purpose of this study is two-fold. First, is to examine the predictive relationship between users’ knowledge of open-source IGTs and the likelihood that the users will upload personal information to social media sites. Second, is to examine the moderating role of users’ demographic factors (gender, academic standing, and age) on the predictive relationship between users’ knowledge of open-source IGTs and the likelihood that the users will upload personal information to social networking sites. The predictor variable is the users’ knowledge of open-source IGTs, the criterion variable is the likelihood that the users will upload personal information to social networking sites, and the moderating variables are the users’ demographic factors. A self-developed survey will be used to measure the study variables using a sample of undergraduate students at a four-year college in the southeastern region of the United States. Descriptive statistics and regression analysis will be conducted to characterize the data and address the research questions and hypotheses of the study using statistical analysis software called IBM SPSS Statistics Version 24.
In this chapter, the researcher will discuss the chosen research method and design, as well as their appropriateness for the study, after which the researcher will present the population, sampling, and data collection procedures. The researcher will next discuss the validity of the data collection procedures, followed by the plan for data analysis. The chapter will conclude with a summary of the highlights of the proposed methodology.
The research method for this study will be quantitative in nature. Researchers use quantitative methods to test hypotheses using numerically measured variables subjected to statistical analysis (Simon, 2011; Watson, 2015). Quantitative methods are normally used when the objective of the research is to examine the relationships between variables and make inferences about the population under study using the statistical results (Barczak, 2015; Simon, 2011). In quantitative research, variables are pre-specified by the researcher which means the definition and operationalization of the variables are known before any data collection commences (Watson, 2015). Quantitative studies delineate the pre-specified variables into either independent/predictor or dependent/criterion variables, where the former being the variable that is changed or controlled while the latter being the variable being tested and measured in research or an experiment (Cooper & Schindler, 2013). In some studies, moderating variables are included which refer to the variables A moderator variable is a third variable that affects the strength of the relationship between an independent/predictor and dependent/criterion variable (Watson, 2015). In this study, the predictor variable is the users’ knowledge of open-source IGTs, the criterion variable is the likelihood that the users will upload personal information to social networking sites, and the moderating variables are the users’ demographic factors. The purpose of this study is to determine how the users’ knowledge of open-source IGTs predict the likelihood that the users will upload personal information to social networking sites and whether user’s demographic factors moderates such predictive relationship, which makes a quantitative research method appropriate (Cooper & Schindler, 2013).
A qualitative method was deemed not to be appropriate for the study. Qualitative studies are primarily considered as an exploratory research, which aims to gain an understanding of underlying reasons, opinions, and motivations of individuals or group of people (Ibanez-Gonzalez, Mendenhall, & Norris, 2014). Qualitative research is also used to uncover trends in thought and opinions, and dive deeper into the problem through the use of unstructured or semi-structured techniques that includes focus groups (group discussions), individual interviews, and participation/observations (Bansel & Corley, 2012; Jason, 2012). However, in this study the objective is to examine underlying relationships between numerically measured variables gathered from validated questionnaires with the use of statistical analysis, rather than conduct interviews or group discussion with the participants to uncover themes or trends about the users’ knowledge of open-source IGTs or the likelihood that the users will upload personal information to social networking sites. Therefore, a quantitative method is more appropriate than a qualitative method for the study.
Due to the objective nature of this study, the researcher will follow a correlational design, which will determine the relationships between the variables. With a correlational design, researchers seek to identify the significance, direction, and magnitude of relationships between variables with the use of correlation and regression analysis (Christensen, Johnson, & Turner, 2011). Other research designs were considered but were excluded for inappropriateness to address the research questions and hypotheses of the study. An experimental design that makes use of a controlled environment was deemed inappropriate, as this study does not warrant the use of an artificial environment or control of variables. A causal-comparative design was deemed inappropriate, as with it researchers compare two or more groups defined by categorical variables in terms of one or more quantified dependent variables in order to assess causation, which does not apply to this study (Cohen, Manion, & Morrison, 2013). Lastly, descriptive research design was deemed inappropriate, as its aims are to describe characteristics of a population or phenomenon being studied with the use of observational and survey methods; however, this study goes over beyond describing the population but instead focuses on examining underlying relationships between variables (Cooper & Schindler, 2013). As such, the researcher will adhere to a quantitative method with a correlational research design and utilize the self-developed survey for measuring the variables and the SPSS for data analysis.
The target population for this study is comprised of undergraduate students in the southeastern region of the United States. The researcher will administer the study specifically in Daytona Beach, Florida. The researcher selected this location because of proximity. The sample frame for this study will be the undergraduate students at a four-year college aged 18 to 25 years. The eligibility criteria for this research sample includes: (1) being between the ages of 18 to 25; (2) being an undergraduate student at a college in the southeast region of United States; (3) being able to access the Internet; and (4) being able to read English.
In the proposed study, the researcher will use purposive sampling to recruit participants. (Suen, Huang, & Lee, 2014). According to Brus and Knotters (2013) as well as Robinson (2014), purposive sampling is a kind of sampling that ensures participants are within the parameters of the study and provides opportunity to select participants who can give richer information to be able to address the objective of the study (Brus & Knotters, 2013; Robinson, 2014). Eligibility of the participants will be ensured through the use of screening questions in the survey. The screening questions correspond to the aforementioned eligibility criteria for the participants.
The researcher conducted a power analysis to determine the minimum required sample size for the study and in doing so considered four factors: (1) the level of significance, (2) the effect size, (3) the power of test, and (4) the statistical technique (Faul, Erdfelder, Buchner, & Lang, 2013). The level of significance refers to the probability of rejecting a null hypothesis given that it is true, which researchers commonly refer to as the Type I error (Haas, 2012). The level of significance is usually denoted with an alpha and, in most quantitative studies is set at 95% (Creswell, 2012). The effect size is an approximated measurement of the magnitude of the relationship between the dependent and independent variables (Cohen, 1988). Berger, Bayarri, and Pericchi (2013) asserted that effect sizes in quantitative studies could be categorized according to small, medium, and large, where the medium is usually used to denote a balance between being too strict (small) and too lenient (large). The power of test refers to the probability that the test correctly rejects a false null hypothesis, thus accepting the alternative hypothesis (Haas, 2012). In most quantitative studies, researchers usually use an 80% power of the test (Haas, 2012). Lastly, the researcher also considered a statistical technique for the computation of the sample size. The intended statistical technique to address the research questions is regression analysis. Using a significance level of 95% (or an alpha level of 0.5), a medium effect size, an 80% power of test, and regression analysis, the minimum sample size is 128; however, the researcher will target at least 150 participants to provide a buffer when missing data and incomplete responses are achieved. The researcher used G*power in the calculation of the minimum sample size.
The researcher will gain access to the list of potential participants from the administrators of Dayton State College and will ask the professors to disseminate the information about the study to their students. In addition, the researcher will request from the administrators to post the information about the study on their school website and bulletin boards within the campus. Permission from the college administrators has been secured for the dissemination of information to the students (see Appendix A). Potential participants will then need to go the survey link provided in any of the aforementioned channels if they want to participate in the study. An informed consent form will be shown on the actual survey will take place. The potential participant should provide consent in the informed consent form in order to progress to the main survey. All potential participants who did not provide consent will be directed to another page that indicates the conclusion of their intention to participate. Once the required sample size is reached, the researcher will extract the information from the survey site by downloading a Microsoft Excel file. Survey Monkey will be utilized for the survey.
The survey questionnaire will contain close-ended questions that will include choices for the respondents to choose from. These questions are easy to answer and provide reliable measurement (Sue & Ritter, 2007). Furthermore, the questions will be mutually exclusive. The survey questions will feature a mixture of contingency, dichotomous, multiple choice, and rating scales (Sue & Ritter, 2007).
The survey contains five sections and has a total of 23 questions. The first section contains one contingency question. The researcher will use the contingency question to determine the willingness of the potential participant to participate in the study (Sue & Ritter, 2007). The contingency question will be dichotomous. Dichotomous questions present the respondents with two possible responses (Sue & Ritter, 2007). The second section contains demographic-related questions. There will be eight demographic-related questions in a multiple-choice format. The third section deals with privacy-related issues of a social media profile. This section contains eight questions, where one question is answerable with a six-point Likert-type scale, and the rest are multiple-choice questions. The fourth section deals with the presence of open-source tools of a social media profile. This section contains three questions, one of which is answerable with a six-point Likert-type scale and the other two are multiple-choice questions. The fifth section deals with time exposure on social networking sites. This section contains three questions, and all are multiple-choice questions.
The independent variable of students’ social media use will be measured using the items in the fifth section of the survey. The dependent variable of students’ concerns regarding open-source intelligence gathering will be measured using the fourth section of the survey. Lastly, the moderating variables will be measured using the second section of the survey.
The researcher will pilot test the self-developed survey questionnaire using another set of participants from Dayton State College, with help from experts in the field to assess the reliability and validity of the instrument. The experts will be the professors known to the researcher who has pronounced knowledge about the field. Three to five experts will be contacted to help in assessing the reliability and validity of the instrument.
Confidentiality and anonymity are one the most stringent requirements in all social research (Sue & Ritter, 2007). The discussion about the confidentiality and anonymity of participant’s participation will be in the introduction of the survey and will assert that the information they provide will not be disclosed to third parties. Additionally, there will be an opportunity for the respondent to refuse to participate in the survey. If a participant decides to opt-out during the survey or after the survey, they have to send an email to the researcher expressing the intention to opt-out from the study. Lastly, the respondents’ responses will remain confidential and anonymous to mitigate privacy threats from tracking cookies and other tracking methods associated with the use of the internet (Sue & Ritter, 2007).
The internal validity of the results of a quantitative research study is heavily based on the instruments used in gathering data. As mentioned in the previous section, the self-developed survey will be pilot tested using another set of participants from Dayton State College to ensure that it possesses high reliability and validity in measuring the study variables. Cronbach’s alpha will be computed, and factor analysis will be conducted to determine the reliability and validity information of the survey, respectively (Bonnett & Wright, 2015; Kiliç, 2016). Assessment of reliability and validity will be done with the help of three to five experts in the field as mentioned above. Further, to guarantee that data will be relevant, the researcher will ensure that the selected participants are currently enrolled as undergraduate students in Dayton State College in the southeastern region of the United States. The list of current undergraduate students will be secured from the college registrar. Making sure that students are currently enrolled and are attending school will make their responses in the survey more relevant.
The external validity of this study is addressed by the transferability of the analyzed results (Burchett, Mayhew, Lavis, & Dobrow, 2013). The results of the quantitative study are specific to a small number of participants and environments, and thus the findings cannot be applied to a wider population; however, other individuals may relate to the findings. The most that the researcher can do is to ensure that the required sample size will be achieved and participants of the study are relevant to the purpose of the study as discussed above. These individuals should be in similar situations as the samples. The reliability of the design and data collection procedures is addressed in the dependability of the data and research methods used. This is accomplished by providing a detailed description of the research data collection methods. Every step on data collection will be thoroughly reviewed and supported by literature so that no biases and mistakes will interplay.
Data will be entered into SPSS version 24 for Windows. Descriptive statistics will be conducted on demographic factors. Frequencies and percentages will be calculated for categorical data including gender, academic standing, and age. Means and standard deviations will be calculated for the study variables. The dataset will then be screened for accuracy, missing values, and outliers. Additionally, the researcher will remove participants missing more than half of the data for the variables of interest.
Research questions and hypotheses will be addressed by using linear regression analysis. The criterion variable is the likelihood that the users will upload personal information to social networking sites while the predictor variable is the users’ knowledge of open-source IGTs. After which, three multiple linear regression analysis will be conducted each having one of the demographic factors (gender, academic standing, and age) as a moderating variable.
Appropriate assumption testing will also take place to ensure the validity of the model and results (Cohen, Cohen, Aiken, & West, 2003). Assumptions of multiple, linear regression are: observations must be independent, the assumption of homoscedasticity, no multicollinearity in independent variables, no significant outliers, and finally that residuals must normally be distributed. There is no possibility of multicollinearity of predictor variables as there is only one predictor variable. The assumption of homoscedasticity will be assessed by visually examining a plot of the residuals against the dependent variables and assessing that the plot is visually random. Systematic changes in the residuals against the dependent variables may indicate a lack of homoscedasticity (Watson, 2015). Significant outliers will be assessed by visually examining a box plot (Creswell, 2014). Outliers will then be removed from data analysis. Finally, the normality of residuals will be checked by examining a histogram of the residuals.
The purpose of this study is two-fold. First, is to examine the predictive relationship between users’ knowledge of open-source IGTs and the likelihood that the users will upload personal information to social networking sites. Second, is to examine the moderating role of users’ demographic factors (gender, academic standing, and age) on the predictive relationship between users’ knowledge of open-source IGTs and the likelihood that the users will upload personal information to social networking sites. The predictor variable is the users’ knowledge of open-source IGTs, the criterion variable is the likelihood that the users will upload personal information to social networking sites, and the moderating variables are the users’ demographic factors. A purposive sample of 150 participants will be recruited to participate in the study. A self-developed survey will be used to measure the variables, which will be uploaded to an online survey site, Survey Monkey, for data collection. The researcher will first conduct a pilot test to ensure the reliability and validity of the survey instrument before using it in the study. The researcher will contact the administrators of Dayton State College and secure permission to conduct the study in their students. The list of potential students will be requested from the administrators and will ask the professors to disseminate the information about the study to their students. Data analysis will involve computing descriptive statistics and perform regression analyses to test the predictive relationship between the study variables. The researcher will analyze the collected data and present the findings in Chapter 4. A complete list of the survey question can be found in the appendix.
Acquisti, A. & Gross, R. (2006). Proceedings of the 6th Workshop on Privacy Enhancing Technologies: Imagined Communities: Awareness, Information Sharing and Privacy on the Facebook. Cambridge, UK: Robinson College.
Acquisti, A. (2004). EC ’04 Proceedings of the 5th ACM Conference on Electronic Commerce: Privacy in Electronic Commerce and the Economics of Immediate Gratification. New York, NY: AMC.
Adler, P., & Kwon, S. (2002). Social capital: Prospects for a new concept. Academy of Management Review, 27 (1), 17–40.
Agger, Ben. “Critical theory, poststructuralism, postmodernism: Their sociological relevance.” Annual Review of Sociology, (1991): 105-131.
Ahy, M. H. (2016). Networked communication and the Arab Spring: Linking broadcast and social media. new media & society, 18(1), 99-116.
Alfino, M., & Mayes, R. (2003). Reconstructing the right to privacy. Social Theory and Practice, 29 (1),1-18.
Almeida, F. (2012). Privacy problems in the online world. IEEE Internet Computing, 16 (2), 4-6.
Amin, R. (2010). The empire strikes back: Social media uprisings and the future of cyber activism. Kennedy School Review, 10, 64-66.
Anderson, C. (2006). The long tail: How endless choice is creating unlimited demand. London, UK: Random House Business Books.
Anderson, P. (2007). What is web 2.0? Ideas, technologies and implications for education. Joint Information Systems Committee Technology and Standards Watch, February, 1-64.
Aslam, A. A., Tsou, M. H., Spitzberg, B. H., An, L., Gawron, J. M., Gupta, D. K., … & Lindsay, S. (2014). The reliability of tweets as a supplementary method of seasonal influenza surveillance. Journal of medical Internet research, 16(11), e250.
Aviram, A. (2003). Regulation by networks. BYU L. Rev., 1179.
Avison, D., & Fitzgerald, G. (2003). Information systems development: Methodologies, techniques and tools. Maidenhead, UK: McGraw Hill.
Avison, D., Kasper, G., Pernici, B., Ramos, I. & Roode, D. (Eds.). (2008). Advances in information systems research, education and practice. New York, NY: Springer.
Awad, F., & Krishnan, S. (2006). The personalization privacy paradox: An empirical evaluation of information transparency and the willingness to be profiled online for personalization, Management Information Systems Quarterly (30) 1, 13-28.
Babbie, E. R. (2013). The practice of social research. Belmont, CA: Wadsworth.
Bagnall, J. (1958). The exploitation of Russian scientific literature for intelligence purposes. Studies in Intelligence, Summer, 45–49.
Bahney, A. (2006, March 9). Don’t talk to invisible strangers. New York Times. Retrieved from http://www.nytimes.com/2006/03/09/fashion/thursdaystyles/09parents.html?pagewante=all&_r=0
Bandura, A. (1988). Organizational applications of social cognitive theory. Australian Journal of Management, 13 (2), 275-302.
Bandura, A. (1991). Social cognitive theory of self-regulation. Organizational behavior and human decision processes, 50 (2), 248-287.
Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual review of psychology, 52 (1), 1-26.
Bansel, P., & Corley, K. (2012). Publishing in AMJ-Part 7: What’s different about qualitative research? Academy of Management Journal, 55, 509-513. Retrieved from http://www.aom.org
Barczak, G. (2015). Publishing qualitative versus quantitative research. Journal of Product Innovation Management, 32(5), 658-658. doi:10.1111/jpim.12277
Bargh, J., & McKenna, K. (2004). The Internet and social life. Annual Review of Psychology, 55 (1), 573–590.
Barnes, S. (2006). A privacy paradox: Social networking in the United States. First Monday, 11 (9-4). Retrieved from http://firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/1394/1312
Bartlett, J., & Miller, C. (2013). The state of the art: A literature review of social media intelligence capabilities for counter-terrorism. Demos, 2013.
Barney, D. (2000). Prometheus wired: The hope for democracy in the age of network technology. Vancouver, Canada: University of British Columbia Press.
Bates, S. C., & Cox, J. M. (2008). The impact of computer versus paper–pencil survey, and individual versus group administration, on self-reports of sensitive behaviors. Computers in Human Behavior, 24(3), 903-916.
Baudrillard, J. (1975). The mirror of production. (M. Poster, Trans.) New York, NY: Telos Press. (Original work published 1973).
Baudrillard, J. (1994). Simulacra and simulation. (S. Glaser, Trans.) Ann Arbor, MI: University of Michigan Press. (Original work published 1981).
BBC. (2005, July 19). News Corp in $580m Internet buy. Retrieved from http://news.bbc.co.uk/2/hi/business/4695495.stm
Bell, S. (1996). Learning with information systems: Learning cycles in information systems development. New York, NY: Routledge.
Beer, D. (2008). Social network (ing) sites… revisiting the story so far: A response to danah boyd & Nicole Ellison. Journal of Computer‐Mediated Communication, 13 (2), 516-529.
Beniger, J. (1986).The control revolution: Technological and economic origins of the information society. Cambridge, Mass.: Harvard University Press.
Berkowitz, L. (1982). Aversive conditions as stimuli to aggression. In Berkowitz, L. (Ed.), Advances in experimental social psychology (pp. 249-288) (Vol. 15). New York, NY: Academic Press.
Bier, W. (1980). Privacy: A vanishing value? New York, NY: Fordham University Press.
Blaikie, N. (2003). Analyzing quantitative data. London, UK: Sage Publication.
Bonett, D. G., & Wright, T. A. (2015). Cronbach’s alpha reliability: Interval estimation, hypothesis testing, and sample size planning. Journal of Organizational Behavior, 36(1), 3-15. doi:10.1002/job.1960
Boorman, S. (1975). A combinatorial optimization model for transmission of job information through contact networks, Bell Journal of Economics, 6 (1),216-249.
Bosco, F. A., Aguinis, H., Singh, K., Field, J. G., & Pierce, C. A. (2015). Correlational effect size benchmarks. The Journal of Applied Psychology, 100(2), 431-449. doi:10.1037/a0038047
Bose, R. (2008). Competitive intelligence process and tools for intelligence analysis. Industrial Management & Data Systems, 108 (4), 510-528.
Bosnjak, M., & Tuten, T. (2001). Classifying response behaviors in web-based surveys. Journal of Computer-Mediated Communication, 6 (3), 0-0. http://dx.doi.org/10.1111/j.1083-6101.2001.tb00124.x
Bourdieu P (2001). The forms of capital. In: Granovetter M & Swedberg R (eds), The sociology of economic life (2nd edn) (pp. 96-111). Boulder, CO: Westview Press.
Bourdieu, P., & Wacquant, L. (1992). An invitation to reflexive sociology. Chicago, IL: University of Chicago Press.
Boyd, D. (2008). Facebook’s privacy train wreck: Exposure, invasion, and social convergence. Convergence: The International Journal of Research into New Technologies, 14, 13–20.
Boyd, D.. & Heer, J. (2006). Proceedings of the Hawaii International Conference on System HICSS-39: Sciences Profiles as Conversation: Networked Identity Performance on Friendster. Kauai, HI: University of Hawai.
Boyd, D. (2004). Proceedings of ACM Conference on Human Factors in Computing Systems: Friendster and publicly articulated social networks. New York, NY: ACMPress.
Boyd, D. (2006). Friends, Friendsters, and top 8: Writing community into being on social network sites. First Monday, 11 (12-4), Retrieved from http://firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/1418/1336
Boyd, D. (2006a, March 21). Friendster lost steam. Is MySpace just a fad? [Apophenia Blog]. Retrieved from http://www.danah.org/papers/FriendsterMySpaceEssay.html
Boyd, D., & Jenkins, H. (2006). MySpace and deleting online predators act (DOPA). MIT Tech Talk.
Boyd, D., & Ellison, N. (2008). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication 13 (1), 210-230.
Brenner, W., & Frederiksen, A. (2001). Computer searches and seizures: Some unresolved issues. Mich. Telecomm. & Tech. L. Rev., 8, 39.
Briscoe, B., Odlyzko, A., & Tilly, B. (2006). Metcalfe’s law is wrong: Communications networks increase in value as they add members-but by how much? Institute of Electrical and Electronics Engineers Spectrum.43 (7), 34-39.
Brus, D. J., & Knotters, M. (2013). Purposive versus random sampling for map validation: A case study on ecotope maps of floodplains in the Netherlands. Ecohydrology, 6(3), 425-434. doi:10.1002/eco.1289
Burchett, H. E. D., Mayhew, S. H., Lavis, J. N., & Dobrow, M. J. (2013). When can research from one setting be useful in another? understanding perceptions of the applicability and transferability of research. Health Promotion International, 28(3), 418-430. doi:10.1093/heapro/das026
Burt, S. (2000). The network structure of social capital. Research in organizational behavior, 22, 345-423.
Carroll, J. (1975). Confidential information sources: Public and private. Los Angeles, CA: Security World Publishing Company.
Carroll, J. (2006). Privacy: Opposing viewpoints. Farmington Hills,MI: Greenhaven Press.
Casman, B. (2011). The right to privacy in light of the Patriot Act and social contract. (Thesis) Retrieved from ProQuest (1500523).
Cassidy, J. (2006). Me media: How hanging out on the Internet became big business. The New Yorker, 82 (13), 50.
CDC to tap social media tools to spread H1N1 flu virus information. (2009, August 25). Retrieved from http://www.ihealthbeat.org/Articles/2009/8/25/CDC-To-Tap-Social-Media-Tools-To-Spread-H1N1-Flu-Virus-Information.aspx
Cerullo, J., & Cerullo, V. (1994).EDP risk analysis. Computer Audit Journal, 1994 (2), 9-30.
Chen, X., & Shi, S. (2009). Proceedings from International Conference on Multimedia Information Networking and Security:A literature review of privacy research on social network sites. Wuhan, CH: IEEE Computer Society doi 10.1109/MINES.2009.268.
Choi, K. (2008). Computer crime victimization and integrated theory: An empirical assessment. International Journal of Cyber Criminology, 2, 308-33.
Christensen, C. (2011). Discourses of technology and liberation: State aid to net activists in an era of ‘Twitter Revolutions’. Communication Review, 14 (3), 233-253.
Christensen, L. B., Johnson, B., & Turner, L. A. (2011). Research methods, design, and analysis. Allyn & Bacon.
Clayton, C. (2013, February 07). Terrorist tweets: how Al Qaeda’s social media move could cause problems. The Christian Science monitor. Retrieved from http://www.csmonitor.com/USA/2013/0207/Terrorist-tweets-how-Al-Qaeda-s-social-media-move-could-cause-problems
Clegg, F. (1998). Simple statistics. Cambridge, UK: Cambridge University Press.
Clinton, W. (President) (1999, January 22). Remarks on Keeping American Secure for the 21st century. Lecture conducted from National Academy of Sciences, Washington D.C..
Cohen, L., Manion, L. & Morrison, K. (2000). Research methods in education (5th ed.) .London, UK: Routledge Falmer
Cohen, L. & Felson, M. (1979). Social change and crime rate trends: A routine activity approach. American Sociological Review, 52,170-83.
Cohen, N. (2008). The valorization of surveillance: Towards a political economy of Facebook. Democratic Communique, 20 (1), 5-22.
Cohen, R. (2003, July 5). Livewire: Web sites try to make internet dating less creepy. Reuters. Retrieved from http://asia.reuters.com/newsArticle.jhtml?type=internetNews&storyID=3041934
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences. Routledge.
Cohen, L., & Felson, M. (1979). Social change and crime trends: A routine activity approach. American Sociological Review, 44, 588-608.
Coleman, S. (1988). Social capital and the creation of human capital. American Journal of Sociological, 94 (Supplement), S95–S120
Cook, C. Heath, F. & Thompson, R. (2000). A meta-analysis of response rates in web or internet-based surveys. Education and Psychological Measurement, 60 (6),821-836.
Cooper, D., & Schindler, P. (2013). Business research methods (12th ed.). New York, NY: McGraw-Hill Higher Education.
Cooter, R. D., & Porat, A. (2004). Decreasing-liability contracts. Journal of Legal Studies, 33, 157-198.
Coppola, N., Hiltz, R., & Rotter, N. (2004). Building Trust in Virtual Teams. IEEE Transactions on Professional Communication, 2 (47), 95-104.
Correa, T., Hinsley, A. W., & De Zuniga, H. G. (2010). Who interacts on the Web?: The intersection of users’ personality and social media use. Computers in Human Behavior, 26 (2), 247-253.
Costello, R., & Kehoe, T. (2005). Five minute intro to REST. xFront.com. PowerPoint presentation available at: http://www.xfront.com/5-minute-intro-to-REST.ppt
Creswell, J. (2008). Research design: QuantitativeQuantitativeQuantitative, quantitative, and mixed methods approaches (3rd ed.). Thousand Oaks, CA: Sage Publications.
Cutillo, L. A., Molva, R., & Strufe, T. (2009). Safebook: A privacy-preserving online social network leveraging on real-life trust. Communications Magazine, IEEE, 47 (12), 94-101.
Daft, L., & Lengel, H. (1984). Information richness: A new approach to managerial behavior and organization design. In B. M. Staw, & L. L. Cummings (Eds.), Research in organizational behavior (pp. 191-233). Greenwich, CT: JAI.
Davidson, C. (2012, December 27). Social media grows across the gulf. Retrieved from http://uncut.indexoncensorship.org/2012/12/gulf-social-media/
Davies, H., 2009. Theory and intelligence reconsidered. In Intelligence Theory: Key questions and debates. Studies in intelligence. London: Routledge, pp. 189-207.
Debatin, B., Lovejoy, P., Horn, A., & Hughes, N. (2009). Facebook and online privacy: attitudes, behaviors, and unintended consequences. Journal of Computer-Mediated Communication, 15 (1),83-108.
Deleting Online Predator Act of 2006, H.R. 5319, 109th Cong. § 2(d) (2006).
Del Fresno García, M., Daly, A. J., & Segado Sánchez-Cabezudo, S. (2016). Identifying the new Influences in the Internet Era: Social Media and Social Network Analysis. Revista Española de Investigaciones Sociológicas, (153).
Dillman, A., & Bowker, K. (2001). The web questionnaire challenge to survey methodologists. In Reips, U. &Bosnjak, M. (eds.). Dimensions of Internet Science. Lengerich, DE: Pabst Science Publishers.
Dillman, D. (2000). Mail and internet surveys: The tailored design method (2nd ed.). New York, NY: Wiley.
Dillman, D., Tortora, R., & Bowker, D. (1998, November 11). Principles for constructing web surveys. Retrieved from http://220.127.116.11/dillman/papers/1998/PrinciplesforConstructingWebSurveys.pdf
Donath, J., & Boyd, D. (2004). Public displays of connection. BT Technology Journal, 22 (4), 71.
Downes, S. (2004). Educational blogging.EduCause Review, 39 (5), 14–26.
Dwyer, C., Hiltz, S. R., & Passerini, K. (2007). Proceedings of AMCIS 2007 :Trust and privacy concern within social networking sites: A comparison of Facebook and MySpace. Keystone, CO: Colorado State University.
Dwyer, C. (2007). Proceedings of HICSS 2007:Digital relationships in the” myspace” generation: Results from a quantitativequantitativequantitative study. Waikoloa, HI: University of Hawaii.
Eckersley, P. (2010, January). How unique is your web browser?. In Privacy Enhancing Technologies (pp. 1-18). Springer Berlin Heidelberg.
Edison Research (2012, June 06). The social habit June 2012. PDF document. Retrieved from http://www.edisonresearch.com/wp-content/uploads/2012/06/The-Social-Habit-2012-by-Edison-Research.pdf
Ellison, B., Steinfield, C. & Lampe, C. (2011) Connection strategies: Social capital implications of Facebook-enabled communication practices. New Media & Society, 13 (6), 873-892.
Ellison, B., Steinfield, C., & Lampe, C. (2007). The Benefits Facebook “Friends:” Social Capital and College Students’ Use of Online Social Network Sites. Journal of Computer Mediated Communication , 12, 1143-1168.
Ellison, B., Vitak, J., Steinfield, C., Gray, R. & Lampe, C. (2011). Negotiating privacy concerns and social capital needs in a social media environment. In S. Trepte & L. Reinecke (Eds.), Privacy Online: Perspectives on Privacy and Self-Disclosure in the Social Web. New York, NY: Springer.
Etling, B., Robert F., & Palfrey, J. (2010). Political change in the digital age: The fragility and promise of online organizing. SAIS Review, 30 (2), 37-49.
Federal Trade Commission. (2010). About identity theft. Retrieved http://www.ftc.gov/bcp/edu/microsites/idtheft/index.html.
Franke, U., & Rosell, M. (2016, May). Social media for intelligence: research, concepts, and results. In SPIE Defense+ Security (pp. 98510H-98510H). International Society for Optics and Photonics.
Festa, P. (2003, November 11). Investors snub friendster in patent grab. Retrieved from http://news.cnet.com/2100-1032_3-5106136.html
Fielding, N., & Cobain, I. (2011). Revealed: US spy operation that manipulates social media. The Guardian, 17.
Finder, A. (2006, June 11). For some, online persona undermines a re´sume´. New York Times. Retrieved July 02, 2015, from http://www.nytimes.com/2006/06/11/us/11recruit.html
Fish, S., Kraut, E., Root, W., & Rice, E. (1992). Proceedings of the ACM CHI `92 Human Factors in Computing Systems Conference: Evaluating video as a technology for informal communication. New York, NY: ACM Press.
Fisher, R. (1984). Information systems security. Englewood Cliffs, NJ: Prentice-Hall.
Fodden, S. ( 2011, November 17). The Anatomy of a Tweet: Metadata on Twitter – Slaw. Retrieved from http://www.slaw.ca/2011/11/17/the-anatomy-of-a-tweet-metadata-on-twitter/
Fogel, J., & Nehmad, E. (2009). Internet social network communities: Risk taking, trust, and privacy concerns. Computers in Human Behavior, 25 (1), 153-160.
Ford, H. (1997). Why CIA analysts were so doubtful about Vietnam. Studies in Intelligence, 1 (1997), 88-88.
Ford, H. (1998). Calling the Sino-Soviet split. Studies in Intelligence, Winter (1998–99), Unclassified Edition 57–71.
Fowler, J. (2002). Survey research methods (3rd ed.). Thousand Oaks, CA: Sage Publications.
FOX19 (2012, September 14). Police: Sexual predator used Facebook to stalk young girls. Retrieved from http://www.14news.com/story/19544964/copsaaron-frye-of-butler-co-used-facebook-to-stalk-young-girls
Fulk, J. & Steinfield, C. (1990). Organizations and Communication Technology. Newbury Park, CA: Sage
Garrett, J. (2005, February 18). Ajax: A new approach to web applications. [PDF document] . Retrieved from https://courses.cs.washington.edu/courses/cse490h/07sp/readings/ajax_adaptive_path.pdf
Garton, L., & Wellman, B. (1995). Social impacts of electronic mail in organizations: A review of the research literature. Communication Yearbook, 18, 434–53.
Garton, L. Haythornthwaite. C., & Wellman, B. (1997). Studying online social networks. Journal of Computer Mediated Communication, 3 (1).
Gellman, B. & Poitras, L. (2013, June 06). U.S., British intelligence mining data from nine U.S. internet companies in broad secret program. Washington Post. Retrieved from http://www.washingtonpost.com/investigations/us-intelligence-mining-data-from-nine-us-internet-companies-in-broad-secret-program/2013/06/06/3a0c0da8-cebf-11e2-8845-d970ccb04497_story.html
Ghannam, J. (2011). Social media in the Arab world: Leading up to the uprisings of 2011. Center for International Media Assistance, 3.
Gladwell, M. (2010, October 04). Small change why the revolution will not be tweeted. The New Yorker, 86 (10). Retrieved from http://www.newyorker.com/reporting/2010/10/04/101004fa_fact_gladwell
Glasow, P. (2005). Fundamentals of survey research methodology. McLean, Virginia: MITER
Goel, S., & Chen, V. (2005). Proceedings of the Information Resource Management Association (IRMA) InternationalConference: Information security risk analysis – A Matrix-Based Approach. San Diego, CA.
Goldberg, S. (2007, May 13). Analysis: Friendster is doing just fine. Digital Media Wire. Retrieved from http://www.dmwmedia.com/news/2007/05/14
Gomberg, L. (2012). The case for privacy: A history of privacy in the United States as seen through a psychological lens and defined by case law and the impact of social media (whatever happened to “its none of your business”?) (Dissertations) Retrieved from ProQuest. (3494370)
Granovetter, M. (1973). The strength of weak ties, American Journal of Sociology, 78 (6), 1360-1380.
Granovetter, M. (1995) Getting a job: A study of contacts and careers (2nd ed.). Chicago, IL: University of Chicago Press.
Granovetter, S. (1982). The strength of weak ties: A network theory revisited. In P. V. Mardsen & N. Lin (Eds.), Social Structure and Network Analysis (pp. 105–130). Thousand Oaks, CA: Sage Publications.
Green, N. (2003). Outwardly mobile: Young people and mobile technologies. In Katz, J. (Ed.), Machines that become us: The social context of personal communication technology (pp. 201-219). New Brunswick, NJ: Transaction Publishers.
Gross, R., & Acquisti A. (2005). Proceedings ACM Workshop on privacy in the Electronic Society. Information revelation and privacy in online social networks. Alexandria, VA: ACM Press.
Gross, R., & Acquisti, A. (2005). Proceedings in WPES’05: Information revelation and privacy in online social networks. Alexandria, Virginia.
Groves, M., Fowler, J., Couper, P., Lepkowski, M., & Singer, E. (2010). Survey methodology. (2ed.). Hoboken, NJ: John Wiley & Sons Inc.
Hampton, K., & Wellman, B. (2003). Neighboring in Netville: How the internet supports community and social capital in a wired suburb. City & Community, 2 (4), 277–311.
Hansen, P. (2003). Can’t miss-conquer any number task by making important statistics simple. Part1. Types of variables, mean, median, variance and standard deviation. Journal of Health Care Qual 25, (4)19-24.
Hannay, P., & Baatard, G. (2011). Proceedings of the 2nd International Cyber Resilience Conference. GeoIntelligence: Data mining locational social media content for profiling and information gathering. Perth, WA: Security Research Centre.
Haron, H. (2010). Proceeding from Education and Management Technology (ICEMT), 2010 International Conference Cyber stalking: The social impact of social networking technology. Cairo, Egypt.
Harvey, D. (1989). The condition of postmodernity: An enquiry into the origins of cultural change. Cambridge, MA: Blackwell.
Hass, N. (2006, January 08). In your Facebook.com. The New York Times. Retrieve from http://www.nytimes.com/2006/01/08/education/edlife/facebooks.html?pagewanted=all&_r=0
Haythornthwaite, C. (2005). Social networks and internet connectivity effects. Information, Communication, & Society, 8 (2), 125–147.
Haythornthwaite, C., & Wellman, B. (1996). Proceeding in International Sunbelt Social Network Conference: Face-to-Face and Email-to-Email: Work and Friendship in a Networked Organization. San Diego, CA.
Haythornthwaite, C., Wellman, B., & Mantei, M. (1995). Work relationships and media use: A social network analysis. Group Decision and Negotiation, 4, 193-211.
Helliwell, F., & Putnam, D. (2004). The social context of well-being. Philosophical Transaction of the Royal Society, 359 (1449),1435-1446.
Hempel, J., & Lehman, P. (2005). The myspace generation. Business Week, 3963, 88-93.
Henderson, N. (2002). The Patriot Act’s impact on the government’s ability to conduct electronic surveillance of ongoing domestic communications. Duke Law Journal, 52 (1), 179-209.
Heppner, P., Kivlighan, D., & Wampold, B. (1999). Research design in counseling (2nd ed.). New York, NY: Brooks/Cole.
Hertzfeld,A.(2005). Revolution in the valley: The insanely great story of How the Mac was made. Sebastopol, CA:O’Reilly Media.
Hitchcock, J. (2008). Public or private? A social cognitive exploratory privacy on social networking sites. (Thesis). Retrieved from ProQuest. (1452787)
Hitchcock, J. (2014 October 22). Working to Halt Online Abuse Online Harassment/Cyberstalking Statistics. Retrieved from http://www.haltabuse.org/resources/stats/
Hodge, N. (2009, April 08). Inside Moldova’s Twitter revolution. Retrieved from http://www.wired.com/dangerroom/2009/04/inside-moldovas/
Holt, T. J., & Bossler, A. M. (2009). Examining the applicability of lifestyle-routine activities theory for cybercrime victimization. Deviant Behavior, 30, 1-25.
Hoofnagle, C. J., King, J., Li, S., & Turow, J. (2010). How different are young adults from older adults when it comes to information privacy attitudes and policies?. Available at SSRN 1589864.
Hugl, U. (2011). Reviewing person’s value of privacy of online social networking. Internet Research, 21 (4), 384-407.
Hui, J. Y. (2016). Social Media Analytics for Intelligence and Countering Violent Extremism. Combating Violent Extremism and Radicalization in the Digital Era, 328.
Hulnick, A. (1999). Fixing the spy machine: Preparing American intelligence for the twenty-first century. Westport, CT: Praeger.
Hunt, D. (2000). Private investigation: An effective response to the national crime challenge. In J. Richardson (Ed.), Police and private security: What the future holds. Ottawa, Canada: Police Futures Group, Canadian Association of Chiefs of Police.
Ibanez-Gonzalez, D. L., Mendenhall, E., & Norris, S. (2014). A mixed methods exploration of patterns of healthcare utilization of urban women with non-communication disease in South Africa. BMC Health Services Research, 14, 658-673. doi:10.1186/s12913-014-0528-y
Information age: Egypt’s revolution by social media. (2011, February 14). The Wall Street Jounral, Retrieved from http://online.wsj.com/article/SB10001424052748703786804576137980252177072.html
Iranian blogger who told supreme leader Khamenei ‘your Judical system… Is nothing but a slaughterhouse’ tortured to death in prison. (2012, November 19). Retrieved from http://www.memri.org/report/en/0/0/0/0/0/0/6819.htm
Isaac, S., & Michael, B. (1997). Handbook in research and evaluation: A collection of principles, methods, and strategies useful in the planning, design, and evaluation of studies in education and the behavioral sciences (3rd ed.). San Diego, CA: Educational and Industrial Testing Services.
Jacob C., Patricia C., Stephen, W., & Leona, A. (2003). Applied multiple regression/Correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Erlbaum.
Jaeger, T., John B., & Charles M. (2003). The impact of the USA Patriot Act on collection and analysis of personal information under the Foreign Intelligence Surveillance Act. Government Information Quarterly, 20, 295-314.
Jamieson, S. (2004). Likert scales: How to (ab)use them. Medical Education, 38 (9),1212-1218.
Jason, C. H. (2012). Reconciling rigor and range: Observations, experiments, and quasi-experiments in field primatology. International Journal of Primatology, 33(3), 520-541. doi:10.1007/s10764-011-9550-7
Jeong, Y., & Kim, Y. (2017). Privacy concerns on social networking sites: Interplay among posting types, content, and audiences. Computers in Human Behavior, 69, 302-310. doi:10.1016/j.chb.2016.12.042
Johnson, D. (2005, October 17). AJAX: Dawn of a new developer: The latest tools and technologies for AJAX developers. Retrieved from http://www.javaworld.com/javaworld/jw-10-2005/jw-1017-ajax.html
Jump, K. (2005, September 01). A new kind of fame. The Columbian Missourian. Retrieved from http://www.columbiamissourian.com/a/85954/a-new-kind-of-fame/
Junglas, I., & Watson, R. (2008). Location-based services. Communications of the ACM, 51 (3), 65-69.
Kadi, I. (2007). Proceedings in VoIP Communications & Information Technology Commission Conference: Voip policy and regulation regional perspective. Riyadh, SA.
Kaplan, A., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business Horizons, 53 (1), 59-68.
Kant, I. (1998). Groundwork of the Metaphysics of Morals. M. Gregor (Ed.). New York, NY: Cambridge University Press.
Katz, J. E. (Ed.). (2003). Machines that become us: The social context of personal communication technology. New Brunswick, NJ:Transaction Publishers.
Kavanaugh, A., Carroll, M., Rosson, B., Zin, T., & Reese, D. (2005). Community networks: Where offline communities meet online. Journal of Computer-Mediated Communication 10(4), article 3
Kellner, D., & Best, S. (1991). Postmodern Theory. New York, NY: The Guilford Press.
Kahn, D. (2008). An historical theory of intelligence. In Intelligence Theory: Key questions and debates. London: Taylor & Francis Ltd, pp. 4-15.
Khare, R. (2006). Microformats: The next (Small) thing on the semantic web? IEEE Internet Computing, 10 (1), 68-75.
Kiesler, S., & Sproull, L. (1992). Group decision making and communication technology. Organizational Behavior & Human Decision Processes, 52, 96-123.
Kiliç, S. (2016). Cronbach’s alpha reliability coefficient. Journal of Mood Disorders, 6(1), 47. doi:10.5455/jmood.20160307122823
Kittleson, M. (1997). Determining effective follow-up of e-mail surveys. American Journal of Health Behavior, 21 (3), 193-196.
Klemperer, P. (2006). Network effects and switching costs: Two short essays for the new palgrave. Social Science Research Network, Working Paper series.
Kluemper, H., & Rosen, A. (2009). Future employment selection methods: evaluating social networking web sites. Journal of managerial Psychology, 24(6), 567-580.
Knight, M. (2012, September 18). United States presidential race and the social media battle. The Huffington Post. Retrieved from http://www.huffingtonpost.com/mark-knight/us-presidential-race-social-media_b_1889883.html
Koehler, J. (1999). STASI: The untold story of the East German secret police. Boulder, CO: Westview Press.
Kozinets, R. V. (1998). On netnography: Initial reflections on consumer research investigations of cyberculture. NA-Advances in Consumer Research Volume 25.
Kraemer, L., & Dutton, W. (1991). Survey research in the study of management information systems. In K.L. Kraemer (Ed.), The Information Systems Research Challenge: Survey Research Methods (Vol. 3) (pp. 3-58). Cambridge, MA: Harvard Business School Press
Krasnova, H., Spiekermann. S., Koroleva, K. , & Hildebrand, T. (2010). Online social networks: why we disclose. Journal of Information Technology, 25, 109–125.
Krasnova, H., Veltri, N. F., & Günther, O. (2012). Self-disclosure and privacy calculus on social networking sites: The role of culture: Intercultural dynamics of privacy calculus.Business & Information Systems Engineering, 4(3), 127-135. doi:10.1007/s12599-012-0216-6
Krause, J., Croft, D. P., & James, R. (2007). Social network theory in the behavioural sciences: potential applications. Behavioral Ecology and Sociobiology, 62 (1), 15-27.
Kumaraguru, P. & Cranor, F. (2005). Privacy indexes: A survey of Westin’s studies. Pittsburgh, PA: Carnegie Mellon University.
Lampe, C., Ellison, N. & Steinfield, C. (2007). Proceedings of the 20th Anniversary Conference on Computer Supported Cooperative Work: A face(book) in the crowd: Social searching versus social browsing. New York, NY: ACM Press.
Landes, W. & Lichtman, D. (2003). Indirect Liability for Copyright Infringement: An Economic Perspective. Harvard Journal of Law and Technology, 16 (2), 395-409.
Langton, L., & Baum, K. (2010). Identity Theft Reported by Households, 2007. Washington, DC: US Department of Justice.
Larose, D. T. (2014). Discovering knowledge in data: an introduction to data mining. John Wiley & Sons
Laurent, A. M. S. (2004). Understanding open-source and free software licensing. ” O’Reilly Media, Inc.”.
Lavrakas, P. J. (2008). Encyclopedia of Survey Research Methods: AM (Vol. 1). Sage
Lenhart, A. & Madden, M. (2007). Social networking websites and teens: An overview. [PDF document]. Washington, DC: Pew Internet & American Life Project. Retrieved from http://www.pewinternet.org/files/old-media/Files/Reports/2007/PIP_Teens_Privacy_SNS_Report_Final.pdf.pdf
Lewis, J. (2009). MI6 Chief Blows His Cover as Wife’s Facebook Account Reveals Family Holidays, Showbiz Friends, and Links to David Irving. Daily Mail. Retrieved from http://www.dailymail.co.uk/news/article-1197562/MI6-chief-blows-cover-wifes-Facebook-account-reveals-family-holidays-showbiz-friends-links-David-Irving.html
Lewis, K., Kaufman, J., & Christakis, N. (2008). The taste for privacy: An analysis of college student privacy settings in an online social network. Journal of Computer-Mediated Communication, 14 (1), 79-100.
Lewis, K., Kaufman, J., Gonzalez, M., Wimmer, A., & Christakis, N. (2008). Tastes, ties, and time: A new social network dataset using Facebook. com. Social networks, 30(4), 330-342.
Lichtman, D. & Posner, E. (2004). Holding Internet Service Providers Accountable. John M. Olin Law & Economics Working Paper No. 217.
Lichtman, D., & Picker, R. C. (2002). Entry Policy in Local Telecommunications: Iowa Utilities and Verizon. The Supreme Court Review, 41-93.
Liebowitz, J., & Margolis, S. (1994). Network externality: An uncommon tragedy. Journal of Economic Perspectives, 8 (2), 133-150.
Lindqvist, J., Cranshaw, J., Wiese, J., Hong, & Zimmerman, J. (2011). Proceedings of the SIGCHI Conference on Human Factors in Computing Systems: I’m the mayor of my house: Examining why people use a social-driven location sharing application. New York, NY: AMC
Liu, L. (2008). Friendster moves to Asia. Time. Retrieved from: http://content.time.com/time/business/article/0,8599,1707760,00.htmlLinkfluence. (2016). Made in China: the world’s largest social media landscape. Retrieved from: http://linkfluence.com/en/2016/02/12/made-in-china-the-worlds-largest-social-media-landscape/
Livingstone, S. (2006). Children’s privacy online: Experimenting with boundaries within and beyond the family. In R. Kraut, M. Brynin, & S. Kiesler (Eds.), Computers, Phones, and the Internet: Domesticating Information Technology (pp. 128–44). Oxford, UK: Oxford University Press.
Livingstone, S. (2008). Taking risky opportunities in youthful content creation: teenagers’ use of social networking sites for intimacy, privacy and self-expression. New Media & Society, 10 (3), 339-411.
Lozar-Manfreda, K., Bosnjak, M., Haas, I. & Vehovar, V. (2008). Web surveys versus other survey modes: A meta-analysis comparing response rates. International Journal of Market Research, 50 (1), 79-104.
Lyotard, F. (1984). The postmodern condition: A report on knowledge. (G. Bennington & B. Massumi, Trans.) Minneapolis, MN : University of Minnesota Press. (Original work published 1979).
Lyon, D. (1994). The electronic eye: The rise of surveillance society. Minneapolis, MN: University of Minnesota Press.
Lyon, D. (Ed.). (2003). Surveillance as social sorting: privacy, risk, and digital discrimination. New York, NY: Routledge.
Madden, M. (2012). Privacy Management on Social Media Sites. [PDF document]. Washington, DC: Pew Internet & American Life Project. Retrieved from http://www.pewinternet.org/files/old-media/Files/Reports/2012/PIP_Privacy_management_on_social_media_sites_022412.pdf
Madden, M., & Smith, A. (2010). Reputation management and social media: How people monitor their identity and search for others online. [PDF document]. Washington, DC: Pew Internet & American Life Project. Retrieved from http://www.pewinternet.org/~/media//Files/Reports/2010/PIP_Reputation_Management_with_topline.pdf
Madden, M., Lenhart, A., Cortesi, S., Gasser, U., Duggan, M., Smith, A., & Beaton, M. (2013). Teens, social media, and privacy. Pew Research Center, 21.
Magesh, S. & Nimala, K. (2016). A Survey on Machine Learning Approaches to Social Media Analytics. International Journal of Applied Engineering Research, 11 (4), 2411-2416.
Manap, N. A., Rahim, A. A., & Taji, H. (2015). Cyberspace identity theft: The conceptual framework. Mediterraneuan Journal of Social Sciences, 6(4), 595-605. doi:10.5901/mjss.2015.v6n4s3p595
Marcum, C., Ricketts, M., & Higgins, G. (2010). Assessing sex experiences of online victimization: An examination of adolescent online behaviors using routine activity theory. Criminal Justice Review, 35 (4), 412-437.
Marcus, A., Bernstein, M. S., Badar, O., Karger, D. R., Madden, S., & Miller, R. C. (2011, May). Twitinfo: aggregating and visualizing microblogs for event exploration. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 227-236). ACM.
Marquis, G. (2003). Private security and surveillance: From the dossier society to database networks. In Lyon, D. (Ed.), Surveillance as social sorting: privacy, risk, and digital discrimination (pp.226-248). New York, NY: Routledge.
Marsden, P. & Campbell, K. (1984). Measuring tie strength. Social Forces, 63 (2), 482-501.
Maslow, H. (1950). Self-actualizing people: A study of psychological health. Personality Symposia: Symposium #1 on Values, pp. 11-34.
Matei, S. A. (2006). Social cognitive theory, social learning, self-efficacy and social media. Decision Support Systems, 42(3), 1872-1888.
McCue, C. (2006). Data mining and predictive analysis: Intelligence gathering and crime analysis. Burlington, MA: Butterworth-Heinemann.
McCue, C. (2014). Data mining and predictive analysis: Intelligence gathering and crime analysis. Butterworth-Heinemann.
McGrath, J. E. (1984). Groups: Interaction and performance. Inglewood, NJ: Prentice Hall, Inc.
McGrath, J. E. (1990). Time matters in groups’, in intellectual teamwork: Social and technological foundations of cooperative work. J. Galegher, R. Kraut, & C. Egido, Lawrence (Eds.). Hillsdale, NJ: Erlbaum.
McGrath, J. E. (1991). Time, interaction, and performance (TIP): A theory of Groups. Small Group Research, 22 (2), 147–174.
McGrath, S. (2006). The web service and SOA proposition. JISC Technology and Standards Watch, February.
McIntyre, J. (1999). The practical skeptic: Core concepts in sociology. Mountain View, CA: Mayfield Publishing.
Mellenbergh, G. (2008). Surveys. In H.J. Adèr & G.J. Mellenbergh (Eds.), Advising on Research Methods: A consultant’s companion (pp. 183–209). Huizen, The Netherlands: Johannes van Kessel Publishing.
Mercado, S. (2004). Sailing the sea of OSINT in the information age. Studies in Intelligence, 48 (3),45-55.
Meredith, J. (2013). 10 Chinese social media sites you should be following. Synthesio. Retrieved from: http://www.synthesio.com/blog/10-chinese-social-media-sites-you-should-be-following/
MIDDLE EAST: Social media outwit authoritarianism. (2011).United Kingdom: Oxford Analytica Ltd. Retrieved from http://search.proquest.com/docview/850433963?accountid=44888
Miles, K. (2012, June 28). Kourtney Reppert, LA model, Facebook stalked by man threatening to kill her (photos, video). The Huffington Post. Retrieved from http://www.huffingtonpost.com/2012/06/28/kourtney-reppert-la-model-facebook-stalked_n_1634557.html
Miller, E., & Dollard, J. (1941). Social learning and limitation. New Haven, CT: Yale University Press.
Miller, F. (2012). Transforming learning environments: Strategies to shape the next generation. Bingley, UK: Emerald.
Miller, P. (2005). Web 2.0: Building the new library. Ariadne, 45 (October), 1-6.
Ministry of information undersecretary orders blocking of 1,040 websites. (2009, September 23). Retrieved from http://www.ifex.org/bahrain/2009/09/23/websites_blocked/
Miro, F. (2014). Routine Activity Theory. Encyclopedia of Theoretical Criminology. doi:10.1002/9781118517390.wbetc198
Misner, R. (1999). The world’s best known marketing secret: Building your business with word-of-mouth marketing (2nd ed.). Austin, TX: Bard Press.
Montgomery, J. (1991). Social networks and labor market outcomes. The American Economic Review, 81 (5), 1408-1418.
Moor, J. (1990). The ethics of privacy protection. Library Trends, 39, (1 &2), 69–82.
Moor, J. (1997). Towards a theory of privacy in the information age. Computers and Society, 27 (3), 27–32.
Moore, D. (1963). Open-sources on soviet military affairs. Studies in Intelligence, Summer, declassified article, 101-101.
Moore, M. (2010, December 30). China makes Skype illegal. Retrieved from http://www.telegraph.co.uk/technology/internet/8231444/China-makes-Skype-illegal.html
Morris, M., & Ogan, C. (1996). The Internet as mass medium. Journal of Computer‐Mediated Communication, 1 (4), 0-0.
Mortensen, T., & ProQuest Information and Learning Company. (2012). Online networks in process change and innovation.
Mosco, V. (1996). The political economy of communication: Rethinking and renewal. London, UK: Sage.
Myers, C., & Shultz, G. (1951). The dynamics of a labor market. New York, NY: Prentice-Hall
Nagel, A. C., Tsou, M. H., Spitzberg, B. H., An, L., Gawron, J. M., Gupta, D. K., … & Sawyer, M. H. (2013). The complex relationship of realspace events and messages in cyberspace: case study of influenza and pertussis using tweets. Journal of medical Internet research, 15(10), e237.
Naim, H. (2011, May 17). Social media creating social awareness in the Arab world. Retrieved from http://www.commongroundnews.org/article.php?id=29759&lan=en&sp=0
Nie, H. (2001). Sociability, interpersonal relations, and the Internet: Reconciling conflicting findings. American Behavioural Scientist, 45 (3), 420–35.
Nielsen Wire. (2012, June 22). May 2012-top U.S. web brands and news websites. [Web blog]. Retrieved from http://blog.nielsen.com/nielsenwire/online_mobile/may-2012-top-u-s-web-brands-and-news-websites/
O’Callaghan, D., Greene, D., Conway, M., Carthy, J., & Cunningham, P. (2013). An analysis of interactions within and between extreme right communities in social media. In Atzmueller, M., Chin, A., Helic, D., & Hotho, A. (Eds.), In Ubiquitous Social Media Analysis (pp. 88-107). Berlin, Heidelberg: Springer.
O’Donnell, C. (2011, September 12). New study quantifies use of social media in Arab Spring. Retrieved from http://www.washington.edu/news/2011/09/12/new-study-quantifies-use-of-social-media-in-arab-spring/
O’Keeffe, G., & Clarke-Pearson, K. (2011). The impact of social media on children, adolescents, and families. Pediatrics, 127 (4), 800-804.
Oliver, E., Wilson, J., & Slater, T. (1972). Practical security in commerce and industry. New York, NY: Wiley.
Omand, D., Bartlett, J., & Miller, C. (2012). A balance between security and privacy online must be struck…. Demos, 13.
O’Reilly, T. (2007). What is Web 2.0: Design patterns and business models for the next generation of software. Communications & Strategies, 1 (First Quarter), 17.
Oscar, G. (1993). The panoptic sort: A political economy of personal information. Boulder, CO: Westview.
O’Shea, W. (2003, June 4-10). Six Degrees of sexual frustration: Connecting the dates with Friendster.com. Village Voice. Retrieved from http://www.villagevoice.com/2003-06-03/news/six-degrees-of-sexual-frustration/full/
Parker, B. (1981). Managers guide to computer security. Reston, VA: Prentice-Hall.
Parks, R., & Floyd, K. (1996). Making friends in cyberspace. Journal of Computer-Mediated Communication, 1 (4),0-0.
Paxton, P. (1999). Is social capital declining in the United States? A multiple indicator assessment. American Journal of Sociology, 105 (1), 88–127.
Pennock, J., & Chapman, W. (Eds.). (1971). Nomos XIII: Privacy. New York, NY: Atherton Press.
Perkel, D. (2006). Copy and paste literacy? Literacy practices in the production of a MySpace profile. In K. Drotner, H. S. Jensen, & K. Schroeder (Eds.), Informal Learning and Digital Media: Constructions, Contexts, Consequences. Newcastle, UK: Cambridge Scholars Press.
Pett, A., (1997). Non-parametric statistics for health care research. London, UK: Sage Publications.
Pew Global Attitudes. (2006, February 21). Truly a World Wide Web: Globe going digital (Project Report). Washington, DC: Retrieved from http://www.pewglobal.org/2006/02/21/truly-a-world-wide-web/
Picker, C. (2003). Understanding Statutory Bundles: Does the Sherman Act Come with the 1996 Telecommunications Act? University of Chicago Law School. Copyright (1, 2003).
Picker, R. C. (2004). Cyber security: Of heterogeneity and autarky. University Chicago Law & Economics, Olin Working Paper, (223).
Pinsonneault, A., & Kraemer, L. (1993). Survey research methodology in management information systems: An assessment. Journal of Management Information Systems, 10 (2), 75-105.
Political media in Bahrain: From the murals and publications to the online forums. (2012, January 07). Retrieved from http://bhmirror.hopto.org/article.php?id=2712&cid=117
Post, C. (2001). Three concepts of privacy, faculty scholarship Series, 185. Retrieved from http://digitalcommons.law.yale.edu/fss_papers/185
Pratt, T. C., Holtfreter, K., & Reisig, M. D. (2010). Routine online activity and internet fraud targeting: Extending the generality of routine activity theory. Journal of Research in Crime and Delinquency, 47 (3), 267-296.
ProgrammableWeb. (2013, July 09). Retrieved from http://programmableweb.com/ Putnam, D. (2000). Bowling Alone. New York, NY: Simon & Schuster.
Pugh, S. E. (2017). cloudy with a chance of abused privacy rights: Modifying third party fourth amendment standing doctrine post-spokeo. American University Law Review, 66(3), 971.
Quan-Haase, A. (2007). University students’ local and distant social ties: Using and integrating modes of communication on campus. Information, Communication & Society, 10 (5), 671-693.
Rash, J. (2011, January 30). Rash report: Social media as a tool for social unrest. Star Tribune. Retrieved from http://www.startribune.com/opinion/114834484.html?refer=y
Raynes-Goldie, K. (2010). Aliases, creeping, and wall cleaning: Understanding privacy in the age of Facebook. First Monday, 15 (1).
Read, B. (2006). Think before you share. The Chronicle of Higher Education, 52 (20), A38-A41.
Rees, A., & Shultz, G. (1970). Workers in an urban labor market. Chicago, IL: University of Chicago Press.
Reuter, T., & Cimiano, P. (2012, June). Event-based classification of social media streams. In Proceedings of the 2nd ACM International Conference on Multimedia Retrieval (p. 22). ACM.
Reyns, B. W. (2011). Online routines and identity theft victimization: Further expanding routine activity theory beyond direct-contact offenses. Journal of Research in Crime and Delinquency, 50(2), 216-238. doi:10.1177/0022427811425539
Rice, E., & Love, G. (1987). Electronic emotion: Socioemotional content in a computer-mediated communication network. Communication Research, 14, 85-108.
Riley, S. (2011, October 31). Social media one key to the Arab Spring IT-savvy population it played bigger role in Tunisia, Egypt than in Libya, Yemen, some say. Investor’s Business Daily. Retrieved from http://ic.galegroup.com/ic/suic/NewsDetailsPage/NewsDetailsWindow?zid=783003c6ebe4a49bfac80617b0d53be9&action=2&catId=&documentId=GALE%7CA271013537&userGroupName=nysl_se_jjhs&jsid=6b60640710090743430288c32fc13027
Rindfleisch, A., Malter, A. J., Ganesan, S., & Moorman, C. (2008). Cross-sectional versus longitudinal survey research: concepts, findings, and guidelines. Journal of Marketing Research, 45 (3), 261-279.
Robinson, O. C. (2014). Sampling in interview-based qualitative research: A theoretical and practical guide. Qualitative Research in Psychology, 11(1), 25. doi:10.1080/14780887.2013.801543
Rosenbush, S. (2012, July 13). Chicago police use social media to break wave of killings. [Web blog message]. Retrieved from http://blogs.wsj.com/cio/2012/07/13/chicago-police-use-social-media-to-break-wave-of-killings/
Rottman, B. M., & Hastie, R. (2014). Reasoning about causal relationships: Inferences on causal networks. Psychological Bulletin, 140(1), 109-139. doi:10.1037/a0031903
Russell, J. (2012, June 27). Think Skype is big? Go see how many people are using Tencent’s QQ right now. Retrieved from http://thenextweb.com/asia/2012/06/27/think-skype-is-big-go-see-how-many-people-are-using-tencents-qq-right-now/
Safranek, R. (2012, March 02). The emerging role of social media in political and regime change. Retrieved from http://md1.csa.com/discoveryguides/social_media/review.pdf
Salant, P., & Dillman, A. (1994). How to conduct your own survey. New York, NY: John Wiley and Sons.
Santora, M. (2007, August 7). Giuliani’s daughter shows support for Obama, for a time. New York Times. Retrieved July 2, 2015, from http://www.nytimes.com/2007/08/07/us/ politics/07giuliani.html
Saudi Arabia freedom on the net 2012. (2012). Retrieved from http://www.freedomhouse.org/report/freedom-net/2012/bahrain
Schaurer, F. (2012, October 31). Social media intelligence (SOCMINT) – Same song, new melody?[web blog]. Retrieved from http://osintblog.org/?p=1462
Schillewaert, N., Langerak, F., & Duhamel, T. (1998). Non probability sampling for WWW surveys: A comparison of methods. Journal of the market Research Society, 40 (4), 307-322.
Schrobsdorff, S. (2010, March 13). Predators playground. Newsweek. Retrieved from http://www.newsweek.com/predators-playground-108471
Scott, G.  (2008). The death of privacy: The battle for personal privacy in the courts, the media, and society. New York, NY: ASJA Press.
Sheptycki, J.(2009). Policing, intelligence theory and the new human security paradigm: some lessons from the field. In Intelligence Theory: Key questions and debates. Studies in Intelligence. London: Routledge, pp.166-187.
Shih, T. & Fan, X. (2008), Comparing response rates from web and mail surveys: A meta-analysis. Field Methods, 20 (3),249-271.
Shirky, C. (2011). The political power of social media: Technology, the public sphere, and political change. Foreign Affairs, 90 (1),28-I.
Simon, M. K. (2011). Dissertation and scholarly research: Recipes for success (2011 ed.). Seattle, WA: Dissertation Success. Retrieved from http://www.dissertationrecipes.com
Sims, J. (2009). Defending Adaptive Realism. In Intelligence Theory: Key questions and debates. London: Routledge, pp. 151-165.
Simson, G. (2000). Database nation: The death of privacy in the 21st century. Sebastopol, CA: O’Reilly.
Skinner, F. (1945). The operational analysis of psychological terms. Psychological Review, 52 (5),270-277.
Smith, R. G. (2010). “Identity Theft and Fraud.” pp. 273-301 in Handbook of Internet Crime, edited by Y. Jewkes and M. Yar. Devon, UK: Willan.
Smith, C. (2014, March 23). (April 2014) By the numbers: 143 Amazing Twitter statistics. Retrieved from http://expandedramblings.com/index.php/march-2013-by-the-numbers-a-few-amazing-twitter-stats/2/#.U3uS4U1OWh0
Stanford Encyclopedia of Philosophy. (2002). Privacy. Retrieved from:
Solove, J. (2010, March 15). Prosser and the privacy torts. Retrieved from http://www.concurringopinions.com/archives/2010/03/william-prosser-and-theprivacy-torts.html
Star Tribune. (2012, July 27). Man accused of stalking Crystal Little Leaguers on Facebook for sex. Star Tribune. Retrieved from http://www.startribune.com/local/164058166.html?refer=y
State of the media Kuwait. (2013). Retrieved from http://www.menassat.com/?q=en/media-landscape/state-media-5
Stein, L., & Sinha, N. (2002). New Global Media and Communication Policy: The Role of the State in Twenty-first Century. In L. Lievrouw & S. Livingstone (Eds.), Handbook of New Media: Social Shaping and Consequences of ICTs(pp. 410–31). London, UK: Sage.
Steele, R. (2007). Open-source intelligence. In Johnson, L. (Ed.) Handbook of intelligence studies (pp.129-135). New York, NY: Routledge.
Strahilevitz, J. (2004). A social networks theory of privacy. University of Chicago Law School, John M. Olin Law & Economics Working Paper (230).
Strater, K. & Richter, H. (2007). Proceedings of the 3rd Symposium on Usable Privacy and Security: Examining Privacy and Disclosure in a Social Networking Community. New York, NY: AMC.
Stutzman, F., & Kramer-Duffield, J. (2010). In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Friends only: examining a privacy-enhancing behavior in Facebook. New York, NY: ACM.
Sue, V. & Ritter, L. (2007) Conducting online surveys. Thousand Oaks, CA: Sage Publications.
Suen, L. W., Huang, H., & Lee, H. (2014). A comparison of convenience sampling and purposive sampling. Hu Li Za Zhi, 61(3), 105.
Sullivan, B. (2005, April 29). Kids, blogs and too much information: Children reveal more online than parents know. Retrieved from http://www.nbcnews.com/id/7668788/ns/technology_and_science-security/t/kids-blogs-too-much-information/#.U3PWR01OWh0
Sunde´n, J. (2003). Material Virtualities. New York, NY: Peter Lang.
Surowiecki, J. (2004). The wisdom of crowds: Why the many are smarter than the few and how collective wisdom shapes published location. New York, NY: Random House Inc.
Suvilehto, J. T., Glerean, E., Dunbar, R. I., Hari, R., & Nummenmaa, L. (2015). Topography of social touching depends on emotional bonds between humans. Proceedings of the National Academy of Sciences, 112(45), 13811-13816.
Sweeney, A. (2001). Computational disclosure control: A primer on data privacy protection. (Dissertations) Retrieved from ProQuest. (304758484)
Sweeney, L. (2002). K-Anonymity: A model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-Based Systems, 10 (5), 557–570.
Sweeney, L. (2000). Simple Demographics Often Identify People Uniquely. Carnegie Mellon University, Data Privacy Working Paper 3.
Sykes, J. (1999). The end of privacy. New York, NY: St. Martin’s Press.
Taddicken, M. (2014). The ‘Privacy Paradox’in the social web: The impact of privacy concerns, individual characteristics, and the perceived social relevance on different forms of self‐disclosure. Journal of Computer‐Mediated Communication, 19(2), 248-273.
Tajpour, A., Ibrahim, S., & Zamani, M. (2013). Identity theft methods and fraud types. International Journal of Information Processing and Management, 4(7), 51-59.
Tavani, H., & Moor, J. (2001). Privacy protection, control of information, and privacy-enhancing technologies. Computers and Society, 31 (1), 6–11.
Tepper, M. (2003). The rise of social software. networker, 7 (3), 18-23.
Terrorist tweets: how Al Qaeda’s social media move could … (n.d.). Retrieved from http://www.csmonitor.com/USA/2013/0207/Terrorist-tweets-how-Al-Qaeda-s-social-me
Thelwall, M. (2008). Social networks, gender, and friending: An analysis of MySpace member profiles. Journal of the American Society for Information Science and Technology, 59 (8), 1321-1330.
Thelwall, M., Buckley, K., & Paltoglou, G. (2011). Sentiment in Twitter events. Journal of the American Society for Information Science and Technology, 62(2), 406-418.
Tidwell, L., & Walther, J. (2002). Computer-mediated communication effects on disclosure, impressions, and interpersonal evaluations: Getting to know one another a bit at a time. Human Communication Research, 28 (3), 317–348.
Tightly-controlled social media access to replace censorship in Iran. (2013, January 06). Retrieved from http://www.middle-east-online.com/english/?id=56291
Totenberg, N. (2013 February 26). Supreme Court makes it harder to challenge foreign intelligence surveillance act. Retrieved from http://www.npr.org/2013/02/26/172998760/supreme-court-makes-it-harder-to-challenge-foreign-intelligence-surveillance-act
Totten, J. W., Phelps, L. D., Schuldt, B., Deville, J., & Brady, J. (2016). Creating intra-course learning communities using a social media networking site. Journal of the Academy of Business Education, 17, 109.
Trinckes, J. (2012). The definitive guide to complying with the HIPAA/HITECH privacy and security. Boca Raton, FL: CRC Press.
Trusov, M., Bucklin, R., & Pauwels, K. (2009). Effects of word-of-mouth versus traditional marketing: Findings from an internet social networking site. Journal of Marketing, 73 (September) 90-102.
Tudjman, M. & Mikelic, N. (2003). Proceedings of Informing Science+ Information Technology Education: Information science: Science about information, misinformation and disinformation. Pori, Finland: INSITE
Turner, J. H., & Turner, P. R. (1991). The structure of sociological theory (5th ed., p. 512). Belmont, CA: Wadsworth.
Tufekci, Z. (2008). Can you see me now? Audience and disclosure regulation in online social network sites. Bulletin of Science, Technology & Society, 28 (1), 20-36.
Twitter. (2016). Twitter usage: company facts. Retrieved from: https://about.twitter.com/company
Utz, S., & Kramer, N. C. (2009). The privacy paradox on social network sites revisited: The role of individual characteristics and group norms. Journal on Psychosocial Research on Cyberspace , 3 (2).
Valli, C., & Hannay, P. (2010). Proceedings of the 2010 International Conference on Security & Management: Geotagging where cyberspace comes to your place. Las Vegas, NV: CSREA Press.
Van de Ven, .H. , Delbecq, AL. & Koenig, R. (1976). Determinants of coordination modes within organizations. Americal Sociological Review, 41, 322-338.
Van Dijk, J. (2012). The network society (3rd ed.). Thousand Oaks, CA: Sage Publications.
Vehovar, V., Batagelj, Z, & Manfreda, L. (2002). Nonresponse in web surveys, in Groves, R., Dillman, D., Elting, J. and Littlr, R. (Eds.) Survrey Nonresponse (pp. 229-242) New York, NY: Wiley.
Vidalis, S., & Jones, A. (2005). Analyzing: Threat agents and their attributes. Hutchinson, B. (Ed.). In Proceedings of the 5th European Conference on i-Warfare and Security Analyzing (pp. 369-380). London, UK: Academic Conferences Limited.
Von Baeyer, C. (2003). Information: The new language of science. London, UK: Weidenfeld & Nicolson.
Wagner, D., Lopez, M., Doria, A., Pavlyshak, I., Kostakos, V., Oakley, I., & Spiliotopoulos, T. (2010). Proceedings of the 12th international conference on Human computer interaction with mobile devices and services: Hide and seek: location sharing practices with social media. New York, NY: ACM.
Warner, M. (2009). Intelligence as risk shifting. In Intelligence Theory: Key questions and debates. Studies in Intelligence. London: Routledge, pp. 16-32.
Wasserman, S., & Faust., K.(1994). Social network analysis: Methods and applications. Weick, KE: Cambridge University Press.
Watson, B. (1913). Psychology as the behaviorist views it. Psychological Review, 20 (2), 158 177.
Watson, R. (2015). Quantitative research. Nursing Standard, 29(31), 44-48.
Watson, W., Watson, M., & Hopple, W. (Eds.) (1990). United States Intelligence: An Encyclopedia. New York, NY: Garland Publishing Inc.
Weiss, D. (1991). In Proceedings of the 14th National Computer Security Conference: A system security engineering process. Washington, DC.
Wellman, B. (2001). Physical place and cyberplace: The rise of personalized networking. International Journal of Urban and Regional Research, 25 (2), 227-252.
Wellman, B., & Gulia, M. (1997). Net surfers don’t ride alone: Virtual community as community. In P.Kollock & M.Smith (Eds.), Communities in cyberspace. Berkeley, CA: University of California Press.
Wellman, B., & Wortley, S. (1990). Different strokes from different folks: Community ties and social support. American Journal of Sociology, 96, 558–88.
Wellman, B., Haase, A. Q., Witte, J., & Hampton, K. (2001). Does the Internet increase, decrease, or supplement social capital? Social networks, participation, and community commitment. American Behavioural Scientist, 45 (3), 436.
Wellman, B., Salaff, J., Dimitrova, D., Garton, L., Gulia, M., & Haythornthwaite, C. (1996). Computer networks as social networks: Collaborative work, telework, and virtual community. Annual Review of Sociology, 22, 213-238.
Westin, A. F. (1970). Privacy and freedom (Vol. 67). New York, NY: Atheneum.
Wigginton, M., Jensen, C., Vinson, J., & Graves, M. (2014). Kidnapping for Ransom and Social Learning Theory. Homeland Security Rev., 8, 179.
Williams, D. (2006). On and off the ‘net: Scales for social capital in an online era. Journal of Computer-Mediated Communication, 11 (2),
Young, A., & Quan-Haase, A. (2009). In Proceedings of the fourth international conference on Communities and Technologies: Information revelation and internet privacy concerns on social network sites: a case study of Facebook. New York, NY: ACM.
Ying, C. (2013, December 19). Pakistanis use Facebook to kidnap local victims. Retrieved from http://www.thestar.com.my/News/Nation/2013/12/19/Pakistanis-use-Facebook-to-kidnap-local-victims.aspx/
Zhou, B., Pei, J., & Luk, W. (2008). A brief survey on anonymization techniques for privacy preserving publishing of social network data. ACM SIGKDD Explorations Newsletter, 10 (2), 12-22.
Zelin, A. (2013). The state of global jihad online: A quantitativequantitativequantitative, quantitative, and cross-lingual analysis. Washington Institute for Near East Policy.
Appendix A: Literature Search
|Key Word Search||Peer Reviewed Works Reviewed||Germinal Works Reviewed||Books Reviewed||Studies Reviewed|
Research Method and Design
- Quantitative in nature to determine the relationships between and among variables through the use of surveys
- Correlational in research design to identify the significance, behavior, and magnitude of relationships between and among variables
- Self-developed survey with contingency, dichotomous, multiple choice, and rating scales.
- Sample Frame: Undergraduate students
- Minimum Sample Size: 50 undergraduate students
- Cronbach’s alpha must be greater than 0.70 and must possess high validity through factor analysis
Population and Sampling
- Population: College students in the southeastern region of the United States
- Specific Location: Four colleges in Daytona Beach, Florida
- Sample Frame: Undergraduate students who are (a) 18 to 25 years old, (b) currently enrolled in a four-year college in Daytona Beach, Florida, (c) able to access the Internet, (d) able to read and understand English.
- Sampling Technique: Convenience
- Minimum Sample Size: 150 undergraduate students
- Recruitment: (a) obtain a list of potential participants from the administrators of the selected colleges, (b) ask the professors to disseminate the information about the study to their students, (c) postings in school website.
- Potential participants go to Survey Monkey website to sign informed consent form and accomplish the survey.
- Export data from Survey Monkey to Microsoft Excel and then to SPSS Worksheet.
Quantitative Correlational Research
- Pedagogy and technology evaluation
- Contribute to knowledge of leadership of distance education programs
- Contribute to knowledge of qualitative evaluation in education
- Check data set for any missing data and exclude such data points for further analysis.
- Check assumptions for parametric techniques: (a) normality, (b) independence, (c) homoscedasticity, and (d) linearity
- Conduct regression analysis and test the hypotheses.
- Record the results.
Reporting of Results and Conclusion
- Interpret results and link to the identified problem statement and relevant literature
- Provide insights about the results and its implications
- Conclude the study and provide recommendations for further study
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