social media use, fear of crime, and perceived risk of ...

SOCIAL MEDIA USE, FEAR OF CRIME, AND PERCEIVED RISK OF VICTIMIZATION AMONG LIBERAL AND CONSERVATIVE COLLEGE STUDENTS ATTENDING NON-RESIDENTIAL CAMPUSES

By MICHAEL LEE WILLIAMS

A thesis submitted in partial fulfillment of the requirements for the degree of MASTER OF PUBLIC AFFAIRS

WASHINGTON STATE UNIVERSITY School of Politics, Philosophy, and Public Affairs MAY 2018

© Copyright by MICHAEL LEE WILLIAMS, 2018 All Rights Reserved

©Copyright by MICHAEL WILLIAMS, 2018 All Rights Reserved

To the Faculty of Washington State University: The members of the Committee appointed to examine the thesis of MICHAEL LEE WILIAMS find it satisfactory and recommend that it be accepted.

_______________________________________ Mark Stephan, Ph.D., Chair

Laurie Drapela, Ph.D.

Michael Rabby, Ph.D.

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SOCIAL MEDIA USE, FEAR OF CRIME, AND PERCEIVED RISK OF VICTIMIZATION AMONG LIBERAL AND CONSERVATIVE COLLEGE STUDENTS ATTENDING NONRESIDENTIAL CAMPUSES

Abstract

by Michael Lee Williams, M.P.A. Washington State University May 2018

Chair: Mark Stephan With internet connectivity and technology becoming increasingly more mobile and personalized, more people are turning to social media for news and information. With this has come concern over fake news, echo chambers, filter bubbles and, more recently, threats to the democratic process. At issue as well is the potential impact these new technologies may have on perceptions of prominent social problems such as crime and risk. The present study builds upon existing literature in the areas of criminology and media effects by exploring the relationships between social media use, political ideology and fear of crime, perceived risk of victimization, and use of precautionary behaviors among college students attending non-residential college campuses. Consistent with previous literature, results indicate that gender was a predictor of fear of crime and perceived risk. Results suggest that perceived community cohesion is a significant predictor of decreased levels of fear of crime and perceived risk, but also that social media use and political ideology were not significant predictors of fear of crime, perceived risk of victimization or use of precautionary behaviors among respondents. Implications for future research and policy are discussed.

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TABLE OF CONTENTS Page ABSTRACT ................................................................................................................................... iii LIST OF TABLES ......................................................................................................................... vi LIST OF FIGURES ...................................................................................................................... vii CHAPTER CHAPTER ONE: INTRODUCTION ..................................................................................1 CHAPTER TWO: LIVERATURE REVIEW .....................................................................7 The New Media Landscape ...........................................................................................7 Facebook ..................................................................................................................8 Fear of Crime ...............................................................................................................15 Perceived Risk of Victimization ............................................................................16 Community Cohesion ............................................................................................18 Sociodemographic Influences on Fear of Crime and Perceived Risk....................20 Fear of Crime and the Media .................................................................................22 Theoretical Framework ................................................................................................24 Cultivation Theory .................................................................................................25 Routine Activity Theory ........................................................................................28 Hypotheses ...................................................................................................................33 CHAPTER THREE: METHODOLOGY ..........................................................................36 Data ..............................................................................................................................36 Measures ......................................................................................................................37 Dependent Variables ..............................................................................................37

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Independent Variables ...........................................................................................38 Analytic Strategy .........................................................................................................41 CHAPTER FOUR: ANALYSIS ........................................................................................42 Univariate Analysis .....................................................................................................42 Bivariate Analysis ........................................................................................................47 Multivariate Analyses ..................................................................................................54 CHAPTER FIVE: CONCLUSIONS .................................................................................67 REFERENCES ..............................................................................................................................74 APPENDIX MINOR ASSENT DISCLOSURE ....................................................................................99 SURVEY INSTRUMENTS.............................................................................................100 FEAR AND RISK CORRELATIONS ............................................................................108

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LIST OF TABLES Page Table 1: Descriptive Statistics .......................................................................................................46 Table 2: Correlations......................................................................................................................53 Table 3: Regression Analysis: Fear of Crime ................................................................................57 Table 4: Regression Analysis: Perceived Risk of Victimization ...................................................62 Table 5: Regression Analysis: Use of Precautionary Behaviors ...................................................66

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LIST OF FIGURES Page Figure 1: BJS and FBI reports on trends in violent crime and property crime 1993-2015..............5 Figure 2: Supercontrollers and crime .............................................................................................31

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CHAPTER ONE: INTRODUCTION With the rise of technological giants such as Apple, Facebook, and Google, a new era of connectivity and news consumption has been introduced to the American consumer. We are now able to scroll, click, archive, tweet, post, blog, vlog and broadcast from virtually anywhere at any time. Our exposure to news, entertainment, and much else is much more frequent, and with this rapidly evolving mediation of the world around us comes both promise and problems. In the wake of the 2016 general election, the subject of online fake news was raised by pundits, activists and the mainstream media. Since then, the term has become a part of the political lexicon — even to the point that president Trump has credited himself with coining the term (Borchers, 2017). Since the 2016 election, social media and fake news has even been credited by some for influencing the 2016 general election in a minimal but substantive way. Facebook itself has since confirmed selling $100,000 worth of socially divisive ads during the 2016 campaign to a Russian agency known for using “troll” accounts to post on social media and comment on news sites (Shane & Goel, 2017).1 While studies have suggested fake news was not influential in the outcome of the election (Concha, 2017; Allcott & Gentzkow, 2017), a growing number of researchers have undertaken efforts to explore the extent of the impact of fake news on voting behavior during the 2016 election (DiFranzo & Gloria-Garcia, 2017; Groshek & Koc-Michalska, 2017). A recent study by Gunther, Beck and Nisbet (2018) analyzed a sample of voters who cast their ballot for Barack Obama in 2012 and found that belief in select fake news stories was strongly associated with voters defecting from the Democratic ticket in 2016. This relationship

“Trolling” refers to comments on social media threads made specifically to instigate opposition or reaction from other commenters, and the term is typically used when referring to politically-charged posts 1

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was significant even while controlling for factors such as candidate favorability and ideological orientation, suggesting the influence of select news headlines since deemed false did likely have a formidable impact on a close general election, independent of oft-cited alternative explanations for such an unexpected electoral outcome. Clearly the potential for social media to impact the American democratic process is ominous. Additionally, the way in which misinformation and new media can affect not only Americans’ perceptions of individual candidates, but also social and policy issues such as crime, poverty and inequality, deserves equal concern. This potential is gaining notoriety, and Mark Zuckerberg, Facebook’s founder and CEO, recently disclosed that Facebook will be significantly overhauled to address the concerns brought to bear in the wake of the general election, though skeptics question whether the proposed overhaul will only make matters worse (Isaac, 2018). Remaining at issue, however, is the fact that information drives public perceptions of salient social issues which, in turn, drive public opinion and, subsequently, the feedback mechanisms policymakers and social actors rely on to allocate resources, implement and evaluate policies, and make decisions about governance and service delivery. Online social networking sites (SNS) have seen a notable increase in the number of users since the outset of the new millennium (Perrin, 2015). That the potential for misrepresentation of facts and sensationalism is so great in the online world is not a surprise, given the democratization and decentralization of sources available on the World Wide Web. In fact, more and more Americans are turning to online platforms to access and consume news, and a 2016 update of a 2013 Pew research study found that 62% of U.S. adults get their news from social media, with 18% doing so often (Gottfried & Shearer, 2016). Of the 67% of U.S. adults subscribing to Facebook, 66% report getting their news from the site, which equates to roughly

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44% of the U.S. adult population (Gottfried & Shearer, 2016). Facebook is by far the largest SNS in the U.S. in terms of reach, and, while U.S. adults do seek news on other SNS, such as YouTube or Tumblr, less than one-fifth of respondents have reported getting news from these sites (Gottfried & Shearer, 2016). While users of more popular sites such as Twitter or Reddit report getting their news from these sites — 6 in 10 and 7 in 10, respectively — the total number of U.S. adults consuming news from these sites trails that of those who consume news via Facebook (Gottfried & Shearer, 2016; Perrin, 2015). Still, what is striking is the rate at which more U.S. adults are turning to social media to access news. According to the original 2013 Pew study, 66% of Reddit users, 47% of Facebook users, and 52% of Twitter users got their news from each site (Gottfried & Shearer, 2016). Of the three sites, Facebook was found to have the most notable increase-overtime of news consumption by users at 13%, compared to 8% and 7% increases for Reddit and Twitter users, respectively (Gottfried & Shearer, 2016). These results should be of some concern to those researching the social world for many reasons. Traditional journalistic ethical standards, editorial safeguards and accountability mechanisms are not applicable to the maelstrom of both information and misinformation now so widely available at the click of a mouse or the swipe of a finger (Allcott & Gentzkow, 2017). Researchers have pointed out that users are often their own gatekeepers with respect to news choice, and that online marketing strategies and competition among search engines and ecommerce sites are resulting in information segregation and the proliferation of online “filter bubbles,” in which algorithms predict users’ preferences and filter content on that basis, effectively severing users from information that could challenge or contradict their preferences, political or otherwise (Bozdag & van den Hoven, 2015; Diakopoulos, 2015; Pariser, 2011;

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Stroud, 2011). While niche and partisan news sources have been a part of the American social and political milieu for decades in one medium or another, the sheer volume of content on the Internet, and the ease with which it can be accessed, has spurred a paradigm shift in the way we access and consume information (Bozdag & van den Hoven, 2015; Stroud, 2011). The increase in news consumption on social media and the filter bubble problem is one which presents unique challenges to the study of media effects, but it is also one which is inextricably linked to traditional questions and findings on the topic. This is due to the fact that SNS users still consume news via cable television, talk radio, and print media (Allcott & Gentzkow, 2017; Gottfried & Shearer, 2016). This fact only highlights the need for researchers to give serious consideration to the mediating effects of social media news consumption, particularly with respect to perceptions of prominent social, political and policy issues. Research on the potential of social media as a mediating influence on perceptions of these problems can inform policymakers, politicians and civil servants about the utility and appropriate use of official social media accounts, as well as provide valuable insight into the opinions of the public at large. Of these problems crime is arguably among the most salient, in terms of disparities in public perception and the data. According to a 2016 Pew study, 57% of registered voters reported that crime had gotten worse, despite crime data collected by the Bureau of Justice Statistics (BJS) and Federal Bureau of Investigation (FBI) clearly showing a notable decline in violent crime and property crime nationwide since 1993 (Gramlich, 2017). As shown in Figure 1, crime in the U.S. over the last quarter century has declined considerably. Given the apparent disconnect between perception and reality, it is incumbent upon researchers to give a fair hearing to the role new media may be playing in this collective dissonance. Moreover, considering the

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extent to which media and crime have been studied in the U.S., it follows that fear of crime would be a reasonable scale by which to measure the ways in which to social media news consumption mediate individual perceptions of proximate social problems such as crime. Further, given that Facebook is the SNS with the most reach, it makes sense to focus on the potential influences this site has on such perceptions, specifically. However, although extant research has found much support for the influence of traditional media consumption on perceived fear of crime (Gordon & Heath, 1981; Romer, Jamieson & Aday, 2003; Skogan & Maxfield, 1981), there is a stark lack of research concerning the effects of social media news consumption on this condition, and even less dealing with Facebook specifically.

Figure 1: BJS and FBI reports on trends in violent crime and property crime, 1993-2015 (Pew Research Center, 2017).

Research in this area is lacking. Investigations assessing the independent influences of SNS use on perceptions of salient social problems such as crime or perceived risk of victimization are conspicuously absent among this literature. The present study focuses on college students, who prior literature has shown to be generally between the ages of 18 and 19: the age group most likely to be Facebook users (Junco, 2013; Ma & Baum, 2016; Perrin, 2015; National Center for Education Statistics, 2016). Those in this age group are also more likely to engage in both risky and precautionary behaviors, both of which have been shown to influence

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fear of crime and perceived risk (Ferraro, 1996; SAMHSA, 2014a; SAHMSA, 2014b). Further, unlike students who attend college at residential campuses, students who attend non-residential campuses are arguably more likely to be embedded in the communities in which they attend school; that is, they are arguably more likely to have social and familial ties in the same communities in which they are attending school. This allows for a more robust examination of perceived community cohesion and its relationship with fear of crime and perceived risk in this population. This may not be as feasible with a sample of students attending a residential fouryear university campus, where social and familial ties to the community may not be as strong or prominent among students. Because factors such as perceived community cohesion have been shown to influence fear of crime and perceived risk (Sampson, Raudenbush & Earls, 1997), understanding the way in which SNS can mediate these perceptions also requires an examination of a population who frequently engages in an online community. This is because SNS — Facebook in particular — offers many commonly used apps and features such as membership in closed “groups,” as well as the ability to interact members of the community and monitor social activity within it. Further, prior research on perceptions of crime among liberals and conservatives, as well as the varying media consumption habits between the two, provide a reasonable basis for considering political ideology in this context (Garrett, Carnahan & Lynch, 2013; Stroud, 2011; Unnever, Cullen, & Fisher, 2007). Given the ubiquity of SNS interactions, and the importance of both community ties and political ideology when considering perspectives on crime and risk, the question the present study investigates is the extent to which frequency and duration of SNS use influences fear of crime and perceived risk of victimization among liberal and conservative students of nonresidential college campuses.

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CHAPTER TWO: LITERATURE REVIEW The following section explores extant research on the terminology, concepts, and variables utilized in the present study. The chapter begins by exploring the literature on social media usage and the history of SNS, including relevant issues and findings related to SNS use among the population under study. This will be followed by an exploration of the literature on traditional forms of media such as print, radio and television. These findings will be discussed in the context of recent scholarship in the arena of news consumption and political ideology. Following this will be a brief review of the relevant literature investigating the independent variables of interest, as well as a discussion of the relevant literature dealing with perceived risk of victimization and fear of crime. Finally, the potential implications brought to bear on crime control policy and government media relations will be discussed. The New Media Landscape SNS are defined by communications and technology scholars as web-based services which allow individuals to 1) construct a public or semi-public profile within a bounded system, 2) articulate a list of other users with whom they share a connection, and 3) view and traverse their list of connections and those made by others within the system (Ellison, 2007; Ellison, Steinfield & Lampe, 2007). The types of connections, profiles, and conventions vary from site to site, and the distinction between “network” and “networking” is a deliberate choice of terminology among researchers because “networking” implies connection initiation with strangers while research suggests that the goal of most users of large SNS is to connect with those who are already a part of their extended social network (Ellison, 2007; Ellison et al., 2007). The history of SNS is one which is full of diverse typologies, innovations, failures and monumental successes. The first SNS, the now defunct SixDegrees.com, was launched in 1997,

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featured “friends” lists and messaging and, although it attracted millions of users, ultimately failed in 2000 (Ellison, 2007). Between 1997 and 2001, however, several tools sprang up which supported various types of profiles, such as dating and professional sites (Ellison, 2007). This was followed by a wave of professional SNS startups in 2001 such as Ryze.com, LinkedIn and Friendster, which were concentrated largely in Silicon Valley to facilitate connections among the business and technology community (Ellison, 2007). SNS became mainstream from 2003 onward as countless SNS’s such as YouTube, Couchsurfing, Care2, and Last.FM tapped various markets, functions, and niche communities to promote their platforms and user-generated content (Ellison, 2007). User-generated content includes posts that feature videos, text or photographic content. Facebook While some SNS have focused on narrow, niche audiences, others focus on growing their audiences exponentially (Ellison, 2007). Facebook, currently the SNS site that reaches the most U.S. adults, began as a Harvard-only SNS, and users needed a Harvard email to sign up (Ellison, 2007). By 2005, Facebook expanded to include high school students and professionals inside corporate networks, and, unlike other SNS, allowed developers to build “applications” which enabled users to personalize their profiles and perform tasks other SNS could not accommodate (Ellison, 2007). Ultimately, Facebook became a publicly traded company and, as mentioned previously, now reaches more than 67% of U.S. adults (Gottfried & Shearer, 2016; Perrin, 2015). With respect to the proposed study, the most salient feature of Facebook is users’ ability to share

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and comment on content previously shared within their extended social networks, or their respective “news feeds.”2 Facebook users can also comment on updates and stories generated or shared by mainstream media sources, local officials, and government agencies at all levels. As mentioned previously, this new level of connectivity has raised questions and concerns regarding the authenticity of content and the capacity for potentially misleading or false information to go “viral.”3 Considering the reach of SNS such as Facebook, Twitter, and Reddit, and the amount of U.S. adults who report getting their news from these sites (Gottfried & Shearer, 2016), understanding the independent effects of exposure to SNS is arguably of vital importance to policymakers and street-level bureaucrats concerned with public attitudes and perceptions of salient social problems and the potential for more responsive and effective governance. In short, skewed public perception can have adverse impact on policy implementation and public trust. Of the U.S. adults reached by Facebook, college-age adults are the group with the highest percentage of Facebook participation (Greenwood, Perrin & Duggan, 2016; Wang, Chen & Liang, 2011). Although the idle, young, female, upper-middle class college undergraduate is a popular stereotypical Facebook user, researchers have found that over a third of Facebook users in the 18 – 29 age group are male, college seniors or graduate students (Greenwood, Perrin & Duggan, 2016; Valenzuela, Park & Kee, 2009). Valenzuela et al (2009) found that one out of five students identified as being a racial/ethnic minority, and that nearly 95% of students who

A “news feed” is the list of posts curated by both Facebook and the individual user which are comprised of text messages, videos, news content, photographs or application notifications or invitations. These can be generated by individual users or “reposted” from other users, including those shared by news sites, government agencies, or private business “page” accounts users “follow.” 3 “Viral” refers to the rapid sharing and widespread reach of content via SNS. User-generated content now has the capacity to reach millions of users in a matter of minutes or hours within, across, and without SNS. 2

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reported having a Facebook account used it daily. In line with more recent findings (Perrin, 2015), another study of Facebook use among college students found that over 78% of respondents were between the ages of 18 and 22 years old and reported spending an average of 101 minutes per day on Facebook (Junco, 2013). Because of the potential for Facebook use to influence perceptions of crime and risk, these findings underscore the importance of examining the mediating effects of SNS like Facebook. Further, as noted above, because this age group exhibits the most frequent Facebook use, it makes the most sense for researchers interested in this potential to study the college-age population. Mainstream Media As mentioned previously, U.S. adults are not consuming news from SNS alone, just as they are no longer relying solely on traditional sources such as print, television and radio (Gottfried & Shearer, 2016). Prior to 2001, news and entertainment were consumed largely via television programming, and, prior to the advent of television, U.S. adults listened to radio, read both the morning and afternoon newspapers, and relied on their social networks to stay informed about the relevant issues and topics of the day (Vaughn, 2008). Today, mainstream media content is accessible through these traditional mediums, as well as the internet and SNS. Broadcast and print news corporations have had to adapt to the ever-changing landscape of the internet and some only maintain an online presence; moreover, many niche and partisan news outlets began as websites and continue to produce only web content (Stroud, 2011; Vaughn, 2008). Since the founding of the U.S., when newspapers relied on the government to operate, the ownership of media has changed completely. Today, only a handful of corporations own the major national newspapers (Bagdikian, 2014). Many localities still rely on local newspapers for

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information regarding local matters, but corporate control of American media has forced organizations to structure content toward revenue-generation, which has resulted in many negative effects on news content, such as commercial bias, sensationalism, and a decrease in issue-oriented reporting (Bagdikian, 2014; Entman, 1989). Further, the concentration of media power in the hands of very few corporations has given rise to many new forms of alternative press and niche news outlets which cater to specific identities and cultures (Stroud, 2011). Echo Chambers Scholars have long studied the influence of our upbringings, value systems, and other predispositions on our news preferences, and with each wave of new media, more research has been done investigating what guides our outlet selection. Research has shown that not only are our preferences in many ways learned, but that we learn in many instances by taking symbolic or group-based cues from those we trust or with whom we identify because we simply are not capable of acquiring the information on our own (Bullock, 2011; Davis & Dunaway, 2016). Over the course of the last few decades the explosion of partisan media has resulted in individuals on opposite sides of the political spectrum living in their own news environments, resulting in partisan selective exposure to media, in which people turn to politically likeminded news, and selective perception, in which people interpret information in ways consistent with their own political beliefs (Garrett et al., 2013; Stroud, 2011). This information segregation has arguably contributed to the significant drop in public trust of mainstream media outlets. According to 2014 study by Gallup, trust in the media to report the news fully, accurately, and fairly among Democrats dropped to a 14-year low of 54%, while Republicans’ trust in media dropped to 27% — just one point above Republicans’ all-time low of 26% in 2012 (McCarthy, 2014). Researchers have also found evidence that the mere

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existence of the online news market has influenced general levels of trust in news media among those who rely solely on traditional news media outlets for their news (Fisher, 2016; Watkins, 2015, 16). Declining trust in mainstream news, and our tendency toward confirmation bias has arguably made the web a much easier avenue by which to curate personalized news about the world around us. Confirmation bias, according to scholars of social psychology, is the phenomena of seeking information which confirms information seekers’ previously held beliefs, expectations, or desired conclusions (Jonas, Stefan Schulz-Hardt, Frey, & Thelen, 2001). With the proliferation of online content, and the consequent lack of traditional editorial controls, a case can be made too that confirmation bias and selective exposure are ultimately exacerbated by the online media landscape. Although research has suggested that perceived credibility of online sources does not significantly impact intentionally biased information seeking online (Johnson & Kaye, 2013), studies have shown that people who fall on opposite ends of the ideological spectrum tend to search and consume online news differently (Garrett, Gvirsman, Johnson, Tsfati, Neo, & Dal, 2014; Tsfati, Stroud & Chotiner, 2013; Garrett & Stroud, 2014; Yeo, Xenos, & Brassard, 2015). However, research also suggests that people on opposite ends of the spectrum differ in these regards according to which online platform they use to search and consume news, the heterogeneity of their network, and the topics they search (Lee, Choi, Kim, & Kim, 2014; Wojcieszak, 2010). These factors are important to consider not only because of the independent and reciprocal effects of ideology and information seeking, but also because of the lack of traditional editorial safeguards against misinformation and inaccuracy that have become specific to the online medium.

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The Filter Bubble Along with the implication of echo chambers in which consumers pass on and consume news and information selectively and with as little resulting dissonance as possible, more recent developments in marketing strategies by big tech companies like Google, Yahoo, and Facebook present new challenges for individuals seeking information outside of their echo chamber (Pariser, 2011). Although the democratization of information via the Internet presents the potential for a plethora of viewpoints, critics have argued that algorithms used by big online intermediaries decrease information diversity by forming so-called ‘filter bubbles’ (Bozdag & van den Hoven, 2015). These filter bubbles are, according to Pariser (2011), the result of decisions of Internet giants designed to better target advertising to user preferences. Further, personalization algorithms used by Facebook display content of similar perspective, removing opposing views altogether without the consent of users, resulting in users being exposed to news shared by their friends alone (Bakshy, Rosenn, Marlow, & Adamic, 2012; Matsa & Mitchell, 2014; Pariser, 2011). Considering critical evaluations of Internet giants’ use of algorithms to curate content according to user preferences, researchers have undertaken efforts to measure the extent and impact of online filter bubbles according to specific platform usage for information seeking (Yom-Tov, Dumais & Gao, 2013; Garret & Resnick, 2011; Resnick, Garrett, Kriplean, Munson, & Stroud, 2013). To address a gap in empirical evidence of filter bubbles and their impacts, Nikolov, Oliveira, Flammini and Menczer (2015) conducted a quantitative study of a massive dataset of Web clicks and two datasets of shares on Twitter and AOL search clicks. Measuring the diversity of information exposure in the context of news, Nikolov et al (2015) found that the

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diversity of targets reached from social media is significantly lower than those reached from search engines, revealing both collective and individual social bubbles. In this vein, researchers have also investigated the differences between what is provided by algorithms and what is searched for regarding traditionally polarizing topics (Aktolga & Allen, 2013; Munson & Resnick, 2010). Koutra, Bennett and Horvitz (2015) conducted a study of the browsing behavior of 29 million Internet users on the controversial topic of gun control in the wake of the shooting at the Sandy Hook Elementary School and found that, even in the midst of shocking, disruptive events, people use the web to access information they agree with and that domains continue to provide a myopic view of controversial topics, despite such disruptive events. Further, researchers have found evidence that differences in ideological viewpoints can affect internet search behavior, whether a Facebook user will actively search for news beyond their news feed, and what content users share (Lee et al., 2014; Lee & Ma, 2012). While Facebook as an SNS is a relatively new medium, and, as mentioned previously, where most U.S. adults get now get their news, the platform itself is unique in that users can do much more than read or watch news. Users can share, read, view, comment on, and rate content obtained anywhere on the SNS — from news organizations to police departments to local officials to long-lost relatives. This diverse utility has the capability to not only inform users of local crime stories, but also to inform users of national crime reports and localized cues of disorder or danger. Further, the filter bubble effect can cut users off from outside information that may be contradictory to their cultivated, symbolic environments, thereby presenting the potential for individual users to live in segregated worlds in which crime rates and realities of risk are not congruous with the information and symbols they receive.

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This new technology, therefore, has the potential to affect fear and perceptions of risk — either positively or negatively — independent of the realities of the communities in which users live. While some have argued that increased Internet use is associated with weaker social ties and lower levels of social capital (Putnam, 1995; Turkle, 1996; White, 1997), more recent research on SNS suggests that many are motivated to join SNS to keep strong social ties and strengthen weak ones (Acquisti & Gross, 2006; Ellison, Steinfeld & Lampe, 2007). These dimensions of social capital are similar to qualities of SNS interactions, and though research has shown that the effects of Facebook on measures of social capital in college students were small (Valenzuala et al., 2009), recent research has shown that Facebook use has positively influenced levels of social capital in users (Su & Chan, 2017; Burke, Kraut, & Marlow, 2011). Still, the potential for Facebook to mediate social reality is grossly understudied. It is important to understand these technological developments and the way in which information seeking, both online and within users’ SNS, is influenced. With respect to the current study, the intersection of online media exposure and both on and offline activity of social networks is important to consider. Although extensive research has been done on traditional, mainstream media effects on fear of crime and perceptions of risk, the potential for online information segregation and new forms of selective exposure warrant consideration. The following will detail previous literature on the measurement of fear of crime and perceived risk of victimization. Fear of Crime Historically, fear of crime has been a difficult concept to measure because of the subjective nature of the construct (Collins, 2016). In recent years, criminological researchers have identified and investigated many problems with the measurement of fear of crime, most notably that:

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Fear of crime can be characterized according to a number of properties, including intensity (the English language recognizes many degrees of fear: terror, worry, alarm, apprehension, dread), prevalence (the proportion of a population that experiences fear during some reference period), and duration, both among individuals and within social units (e.g., communities, cities, nations) (Warr, 2000, p. 455) To claim that fear of crime is one-dimensional, or simply a consequence of actual crime, is too narrow a definition for any meaningful examination, and empirical tests of fear of crime have identified factors which contribute to fear that can be classified under “i) vulnerability, ii) environmental clues and conditions, iii) personal knowledge of crime and victimization, iv) confidence in the police and the criminal justice system, v) perceptions of personal risk, and vi) seriousness of various offenses” (Box, Hale, & Andrews, 1988, p. 341). Under the heading of perceived personal risk, it is important to note that the fear of crime is conceptually distinct from perceived risk of victimization, or, what Warr (2000) refers to as the “subjective probability of victimization” (p. 454). Research has shown that measures of fear and measures of perceived risk do not measure the same phenomenon or behave the same with respect to other variables (Rountree & Land 1996b; Ferraro, 1995). Essentially, fear is not perceived risk but is a consequence of perceived risk — a reaction to the perceived environment (Warr, 2000). Moreover, Ferraro and LaGrange (1987, 1992) contend that fear of crime is an emotional response of dread or anxiety to crime or symbols associated with crime, and to produce such a reaction, recognition of a situation possessing at least potential danger, real or imagined, is necessary (Ferraro, 1995). Perceived Risk of Victimization Perceived risk of victimization as a dimension is qualitatively different than fear of victimization. DuBow, McCabe and Kaplan (1979) refer to perceived risk as an individual’s

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assessment of crime rates and subsequent probability of victimization. While researchers have employed actual crime statistics to provide estimates of “official” or objective crime risk (Janson & Ryder, 1983), others have included survey items which elicit individuals’ beliefs about their own subjective risk with respect to property or personal crime or specific types of victimization, as well as perceptions of disorder (Ferraro & LaGrange, 1992; Gray, Jackson, & Farrall, 2008; Hinkle J. C., 2015; Kubrin, 2014; Kubrin, 2008; LaGrange & Ferraro, 1989; Sampson, Raudenbush & Earls, 1997; Warr & Stafford, 1983). In terms of perceived risk and its relationship to fear of crime, Ferraro (1995) conducted a meta-analysis and found that perceived risk was a significant predictor of fear of crime when controlling for the influence of related variables. Further, Ferraro (1995) developed an index for both fear of crime and perceived risk with high reliability and versatility with respect to different types of crime. Researchers have also emphasized the importance of conceptually distinct measures of fear of crime; specifically, a) emotional, b) cognitive, and c) behavioral measures of the construct. As such, the present study will employ reliable emotive (fear of crime), cognitive (perceived risk of victimization), and behavioral (precautionary or constrained behaviors) indicators. The current study explores the potential for SNS to mediate variables which have been shown to influence fear of crime, perceived risk and use of precautionary behaviors. The following will detail the independent variables of interest: frequency of Facebook usage, political ideology, and perceived community cohesion. Following this is an overview of definitions and literature-based justifications for the demographic and control variables included in the study. Specifically, sociodemographic variables such as age, gender, race, socio-economic status, media consumption, type of residence, living situation, drug and alcohol use, leisure activities, and prior direct or indirect victimization will be discussed.

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Community Cohesion Criminologists agree that fear of crime and perceived risk of victimization are significantly influenced by community cohesion (Sampson, Raudenbush, & Earls, 1997; Will & McGrath, 1995). More specifically, that perceived community cohesion is the important construct to measure, since an individual’s perception of community cohesion is not wholly dependent on actual factors which indicate a cohesive community with high levels of informal social control (Gibson, Zhao, Lovrich & Gaffney, 2002; Lee & Earnest, 2003; Ross & Jang, 2000).4 Community cohesion has traditionally relied on objective measures of “the average number of social ties among residents, their participation in voluntary associations, the frequency with which community residents meet, borrow tools or other household items, and so on” (Lee & Earnest, 2003, p. 137). The basic argument among sociologists has been that cohesive communities are better able to normatively regulate the activities of their respective residents, and that prevalence of criminal offending is related to variations in community cohesion (Bursik 1988; Bursik & Grasmick, 1993; Sampson & Groves, 1989; Lee & Earnest, 2003). Sampson, Raudenbush and Earls (1997) referred to the effect of community cohesion as “collective efficacy,” or, a willingness of neighbors to help each other, engage in informal surveillance activities, and intervene in deviant activity. Consistent with the logic of routine activity theory, residents of more cohesive communities with higher levels of collective efficacy (and, by extension, guardianship) would be less likely to be victimized. Still, factors which do not translate into actual measures of community cohesion can influence perceptions of

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Different from formal social control mechanisms such as police presence and the law in general, informal social control mechanisms include neighbors taking note of or questioning strangers, neighbors watching over each other’s property, assuming responsibility for the supervision of youth, and intervening in local disturbances (Sampson, 1987)

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community cohesion. Indeed, for an individual to perceive any risk of victimization, they must first perceive the safety of their environment, or the lack thereof (Lee & Earnest, 2003). With respect to the present study, Facebook — whose users can network in groups, share news stories, and follow the activities of local associations, police departments, and community leaders — can influence individual perceptions not only of crime prevalence, but also perceptions about community cohesion and, consequently, fear of crime and perceived risk of victimization. Moreover, newer, less common sites such as “Nextdoor,” which allows users near one another to connect to the same virtual “neighborhood” and post comments to a continuous feed regarding neighborhood activities, crime, disruptions, etc., can potentially influence levels of community cohesion, fear of crime, and constrained behavior.5 Another important variable considered in the present study is prior victimization. Research on fear of crime perceived risk clearly suggests that those who have experienced victimization, or know someone who has, are more likely to perceive a higher subjective probability of victimization (Ferraro, 1995; LaGrange & Farraro, 1989; Smith & Hill, 1991; Weinrath & Gartrell, 1996). The victimization experience is rare for most individuals, and most are exposed to victimization through their friends, relatives, and networks (Mesch, 2000). Further, research suggests that individuals acquainted with significant others who have been victims of crime have a higher perception of risk (Mesch, 2000). To control for this potential mediation of the response variables, the questionnaire used in the proposed study included items asking about prior victimization of respondents and their acquaintances. Arguably, this is also an

Nextdoor claims people use the site to engage in activities such as “quickly get the word out about a break-in, organize a neighborhood watch group, track down a trustworthy babysitter, find out who does the best paint job in town, ask for help keeping an eye out for a lost dog” (About Nextdoor, 2017). 5

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appropriate consideration given the potential for respondents to have been repeatedly exposed to instances of members of their social networks sharing stories of potential or actual instances of victimization on their Facebook accounts. Sociodemographic Influences on Fear of Crime and Perceived Risk To perform any meaningful analysis of fear of crime and perceived risk of victimization, consideration for variations in these variables by demographic characteristics must be given. The overall rate of violent crime in the U.S. has declined from 1993 to 2015 from 79.8 to 18.6 victimizations per 1,000 persons aged 12 and older (U.S. Bureau of Justice Statistics, 2015). The rate of violent crime victimizations per 1,000 males was 15.9 and 21.1 for females, with assault the most commonly reported violent crime (U.S. Bureau of Justice Statistics, 2015). Persons aged 65 and older had a lower rate of violent victimization than any other age group, and blacks, as well as those aged 12-17, and those earning $9,999 or less reported the most instances of violent crime (U.S. Bureau of Justice Statistics, 2015). Rates of violent crime were higher in urban areas than rates in suburban or rural areas, and 2015 saw an overall decrease in property crime (U.S. Bureau of Justice Statistics, 2015). Although researchers have historically found age to have a significant, positive relationship with fear of crime and perceived risk of victimization (Warr, 1984), Ferraro (1995) found that more reliable measures of perceived risk show the elderly are not the age group which manifest the highest levels of fear of victimization; rather, younger people tend to exhibit higher fear of crime (see also LaGrange & Ferraro, 1989). According to Ferraro (1995), these findings are consistent with findings that young people are more at risk of victimization than older people given the nature of their lifestyle and routine activities, depending on the type of crime in question.

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Of the individual characteristics that account for variation in fear of crime and perceived risk of victimization, gender is arguably the most important (Akers et al., 1987; Braungart et al., 1980; Ferraro, 1996; Liska et al., 1988; Stafford & Galle, 1984; Warr, 1984). Although this may seem intuitive, given the prevalence of sexual assault and harassment toward women versus men, researchers have not settled on possible causal mechanisms which lead to greater fear of crime and perceived risk of victimization (Reid & Konrad, 2002). Indeed, there is no lack of evidence that women are more often the victims of sexual assault and intimate partner violence, though women do appear to be at low risk for other types of criminal victimization, according to crime statistics (Reid & Konrad, 2002). Still, research has shown that women engage in more precautionary or constrained behavior than males (Gordon, Riger, LeBailly, & Heath, 1980; Ferraro, 1995; Stanko, 1990). To explain the gendered variation in perceived risk of victimization, researchers have contended that women and men are socialized to recognize differing assumptions about both criminality and victimization (Goodey, 1997; Hollander, 2001), and that male “discounting” of risk and fear obscures true measures of fear of crime and perceived risk (Reid & Konrad, 2002). Historically, race/ethnicity has also been an important consideration for researchers studying fear of crime and perceived risk of victimization. However, extant research has not revealed consistent findings on the impact of race on these measures (Rountree & Land, 1996a, 1996b; Rountree, 1998; Reid & Konrad, 2004). Non-whites have been found to exhibit greater levels of fear of crime (Haynie, 1998; Parker, 2010). Further, people of color tend to live in poorer neighborhoods and, as mentioned above, have higher risk of victimization (U.S. Bureau of Justice Statistics, 2015). Researchers have also found that non-whites’ reported fear of crime is attributed to the influences which are associated with living in economically and racially

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segregated neighborhoods, and that fear is greatest in the presence of environmental cues of neighborhood disorder and incivility — which is a common characteristic of economically depressed neighborhoods often home to minorities (Donnelly, 1988; Skogan & Maxfield, 1981; Reid & Konrad, 2004). Important to note too, however, is that the perceived racial composition of neighborhoods influences whites’ fear of crime more so than any other race/ethnicity (Chiricos et al., 1997a). Fear of Crime and the Media Besides important demographic variables, type and frequency of media exposure and news consumption other than Facebook are also important to consider with respect to their influence on fear of crime and perceived risk of victimization. An extensive body of evidence exists which contends public beliefs about crime are inaccurate mostly because of media distortion in covering crime (Baker, Nienstedt, Everett, & McCleary, 1983), and that this distortion is attributed to the “overemphasis on violent crime, the creation of artificial crime waves, the use of crime news as ‘filler,’ misleading reports of crime statistics, and police control of crime news” (Warr & Stafford, 1983, p. 187). This distortion is, of course, not confined to any one medium, making the increase in social media usage for news consumption a salient consideration for researchers investigating media influence on fear of crime and perceived risk of victimization. Apart from the supply-side distortions found in mass media reports of crime, research also suggests that the public is likely to exaggerate the frequency of infrequent, severe crimes and underestimate the frequency of more frequent, less severe crimes, making mass media a unique amplifier (Warr, 2000). Certain horrific criminal events seize the attention of the public, and though some gain national attention, most concern a city or portion of a city (Warr, 2000).

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Conover and Feldman (1986) found that people may forget the details found in any given media story, but their emotional reaction to that story can be enduring. Slovic, Fischhoff, and Lichtenstein (1980, 1982) attribute the tendency to significantly exaggerate the risk of rare lethal events such as tornadoes or floods or homicide to a common error of judgment arising from the availability heuristic, or, the tendency to judge the frequency of such events by the ease with which they can be recalled or imagined (Kahneman & Tversky, 1982; Warr, 2000). Television has been a central consideration for researchers studying fear of crime. Before Facebook and Reddit, television was the way most Americans got their news (Patterson, 2000) With respect to television reports of non-violent crime, research suggests that individuals tend to make societal, rather than individual, judgements regarding their own risk (Tyler, Cook, & Gordon, 1981). However, as mentioned previously, television news relies heavily on sensational coverage of crime, particularly violence and homicide (Hamilton, 1998; Klite, Bardwell, & Salzman, 1995, 1997), and Romer et al (2003) contends that this coverage “increases fear of crime by cultivating expectations that victimization is both likely and beyond control,” an effect independent of actual trends in crime and viewer characteristics (p. 89). In fact, Romer et al (2003) tested this hypothesis through an analysis of survey data obtained from Philadelphia residents and found that the best explanation between television news exposure and concern about crime is pervasive exposure to violent crime on the television news medium. Prior research suggests that newspaper accounts of crime also influences fear of crime (Gordon & Heath, 1981; Skogan & Maxfield, 1981), but individual reactions to newspaper reports have been found to be affected less by the level of sensationalism and more by whether certain criteria are met by the news stories themselves (Heath, 1984). Specifically, Heath (1984) found that the total number of crime stories had less of an effect on fear of crime than did

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whether the crime story was about a local crime and whether action taken by authorities which made readers less vulnerable had taken place. Similarly, Liska and Baccaglini (1990) found that newspaper coverage of crime in other cities made respondents feel safe by comparison, but also that the group least affected by homicide stories were those who were most likely to experience victimization directly or indirectly through communication with friends or relatives. Theoretical Framework The theoretical framework guiding the present study is two-fold, with empirically-based conclusions considered from both cultivation and routine activity theories. The importance of using both theoretical frameworks is illustrated by the fact that, considering the research problem and question, the independent variables of interest, control variables, and the dependent variables are best described and tested by drawing from relevant theoretical work from both criminology and communications. Further, the units of analysis under investigation in this study are individuals, and the constructs measured are based in both the perceptions held by individual respondents and the demographic, experiential and audience characteristics which inform those perceptions. Given the fact that U.S. adults continue to seek news content from cable television sources (Gottfried & Shearer, 2016), as well as the need to measure media exposure, news consumption, and the subsequent perceptions of participants of the current study, cultivation theory is a logical approach by which to evaluate variables identified in prior literature. Media scholars have argued that the cultivation approach can be applied to any dominant medium (Morgan, Shanahan, & Signorielli, 2014, p. 481). Additionally, the routine activity approach will be used to explain individual variations of the perceived risk of victimization and fear of crime experienced by respondents in relation to their perceptions when controlling for media effects,

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demographic and contextual variables, as well as other relevant variables identified in the literature. This will be done using widely supported measures of both perception and precautionary behaviors. In short, the combined framework will be employed to explain the findings of the present study in the context of prior research conducted by scholars of both criminology and communications. Cultivation Theory Cultivation theory of media effects was developed by George Gerbner in the 1960s to explain the influence of growing up and living in a television-dominated cultural environment (Morgan, Shanahan, & Signorielli, 2008). The theory was the result of Gerbner’s research project commissioned by Lyndon B. Johnson, Cultural Indicators, and focused largely on television’s contributions to viewers’ conceptions of social reality (Morgan et al., 2008). The position asserted by Gerbner (1969) was, in short: [T]hose who spend more time watching television are more likely to perceive the real world in ways that reflect the most common and recurrent messages of the television world, compared to those who watch less television but are otherwise comparable in terms of important demographic characteristics. (Gerbner, 1969; Morgan et al., 2008) “Cultivation,” then, refers to the effect television has on viewers’ conceptions of social reality— independent of other factors (Gerbner, 1969). Gerbner posited that television formed a common symbolic environment that reached a variety of individuals and communities, bringing them together through a shared environment of socialized roles and behaviors (Gerbner, 1969; Griffin, 2012). In other words, Gerbner (1969) argued that televisions mass-produced messaging and imagery formed an environment in which many viewers were, for the first time, able to experience a common national culture — a shared but coherent reality.

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The core assumptions on which cultivation theory rests deal with the medium, audience, and the subsequent functionality of the medium on the audience (Gerbner, Gross, Jackson-Beek, Jeffries-Fox & Signorielli, 1978). With respect to the medium, television, unlike print media, did not require the audience to be literate, and it was much more accessible than other forms of media (Gerbner et al., 1978). Cultivation theory also rests on the positivist position that an objective reality exists outside of subjective interpretations of reality, and that the function of television as a medium on the larger audience is not based in this objective reality, but creates an alternate, symbolic reality (Gerbner et al., 1978). At the outset of the Gerbner’s (1969) Cultural Indicators project, the range of topics and issues investigated was broad, though researchers have historically constrained their investigations to the nature and function of television violence (Morgan et al., 2008). Still, most cultivation analyses have found a stable differential across different populations and variables which shows consistency in the direction initially predicted by the theory (Morgan et al., 2008; Shanahan & Morgan, 1999). However, criticisms of the theory include claims that the small but significant cultivation effects are spurious and influenced by factors affecting both television viewing and social perceptions, or that the effect is reciprocal: that pre-existing social perceptions may influence the content and amount of time spent viewing television (Doob & Macdonald, 1979; Hirsch, 1980; Hughes, 1980; Potter & Chang, 1990; Rubin, Perse, & Taylor, 1988; Shrum, 2007; Wober & Gunter, 1988; Zillmann & Wakshlag, 1985). That there is debate among researchers regarding spurious relationships between television viewing and its relationship with perceptions of social reality is not surprising, given the numerous potential mediators, and the fact that so many resources have been used to influence human behavior and perceptions through television advertising and marketing certainly

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exemplifies a persistent faith in cultivation as a reliable causal framework. However, cultivation does not see television’s contribution to conceptions of social reality as a one-way “push” process; rather, its influences on the composition and structure of the symbolic environment are subtle, complex, and intermingled with other influences (Morgan et al., 2008; Morgan & Shanahan, 1999). In short, cultivation is a continual process of interaction between medium, messages, audiences, and contexts (Morgan et al., 2008). With respect to the present study, cultivation is a reasonable approach by which to explain the findings, considering the previous research examining fear of crime and perceived risk of victimization using this approach. In 1994, Gallup reported that concern about crime reached an historic high, despite police and arrest records showing a decline in violent crime over the course of the 1990s (Romer, Jamieson, & Aday, 2003). Non-news drama has been the focus of cultivation analysts, but researchers have also noted the ways in which television news shapes perceptions of social realities (Iyengar & Kinder, 1987; McCombs, Lopez-Escobar, & Llamas, 2000; Romer, Jamieson, & Aday, 2003). Although some researchers have found that local television news significantly predicts crime-risk perceptions (Heath, 1984; O’Keefe & Reid-Nash, 1987), others have found that cultivation effects of local news have not had a significant impact on fear of crime or perceived risk of victimization when considering demographic and contextual factors (Dowler, 2003; Goidel, Freeman, & Procopio, 2006; Gross & Aday, 2003; Rice & Anderson, 1990; Romer et al., 2003). Regardless, the question remains whether SNS and other new media — which shares features of both television, print media and traditional, corporeal social interaction — is unique in its potential to influence perceptions of crime and risk.

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The Cultivation Effect and Facebook Facebook, among other SMS, is a prime example of the way in which symbols and systems of messaging have been fragmented and are less constrained to macro-level environments. Individuals are less reliant on centralized programming and are more exposed to content that is produced, distributed, and consumed by users both within and without their established, offline social networks. Moreover, these symbols and messaging converge with those at the macro-level via traditional media, making research into the mediating effects of this new technology even more salient. Gerbner (1978) argued that as fundamental shifts in the institutions, structures, or technologies take place, the terms of cultivation will change (Shanahan & Morgan, 1999). Therefore, the present study will seek to add to the current understanding of cultivation theory by assessing the extent to which the intensity, or frequency and duration, of Facebook usage contradicts or supports previous analyses of fear of crime and perceived risk of victimization using the cultivation approach (Escholz, Chiricos, & Gertz, 2003; Intravia, Wolff, Paez, & Gibbs, 2017; Salmi, Smolej, & Kivivuori, 2007). Routine Activity Theory Although media portrayals of crime and victimization — be they delivered via local news, television drama, or fiction — have been shown to influence perceptions of risk and fear of crime (Heath, 1984; O’Keefe & Reid-Nash, 1987), criminological researchers have spent considerable time and energy investigating the demographic and contextual mediators of these perceptions and predictors of actual crime and victimization. Routine activity theory deals with the logical results of heightened concern over crime: fear and constrained behavior — or, adjustments in routine activities to lower risk of victimization (Ferraro, 1995). The major premise of routine activities theory posits:

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[S]tructural changes in activity patterns influence crime rates by affecting the convergence in time and space of three necessary elements for criminal victimization: 1) motivated offenders, 2) suitable targets, and 3) absence of capable guardians against crime. (Cohen & Felson, 1979; Miethe, Stafford, & Long, 1987) Developed initially by Cohen and Felson (1979), the routine activity approach focuses on the circumstances under which crimes are carried out, rather than the characteristics of offenders alone (Cohen & Felson, 1979). With roots in human ecological theory, routine activity theory was developed in response to questions about why rates of urban violent and nonviolent crime had increased absent the economic and social conditions ostensibly necessary for such an increase (Cohen & Felson, 1979). Increased crime in the absence of these structural conditions prompted Cohen and Felson (1979) to argue that criminological researchers should focus on crimes as events which occur in specific spaces at specific times. Their central hypothesis ultimately argues that changes in the structure of routines of everyday life alter the likelihood of a crime occurring, which, in turn, makes control of routine activities a critical issue (Cohen & Felson, 1979). In other words, the lack of either motivated offenders, suitable targets, or capable guardians is sufficient to prevent the successful completion of a direct-contact predatory violation (Cohen & Felson, 1979).6 Since its inception, researchers have built upon routine activity theory to include more nuanced definitions of its core elements with considerations for lifestyle variations and social differentiation (Rountree & Land, 1996b; Stafford & Galle, 1984). Still, the major arguments have been consistent, and the general contention has been that time spent in one’s home generally decreases risk of victimization, while time spent in public settings increases risk (Meier

A direct-contact predatory violation is an illegal act in which “someone definitely and intentionally takes or damages the person or property of another” (Glaser 1971; 4, as quoted in Cohen & Felson, 1979, p. 589). 6

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& Miethe, 1993; Mesch, 2000). Likewise, researchers have also found that time spent outside of the home increases the likelihood of burglary victimization (Hough, 1987). Researchers have also sought to connect the micro and macro dimensions of routine activity/lifestyle approaches, arguing that the community context of everyday activities is also crucial in explaining victimization risk (Sampson & Wooldredge, 1987). In this vein, researchers have found that race/ethnicity, gender, age, informal social control, prior victimization and collective efficacy mediate actual risk of victimization (Lee & Hilinski-Rosick, 2012; Mesch, 2000; Miethe et al., 1987; Sampson et al., 1997; Sampson & Wooldredge, 1987). With respect to prior victimization specifically, research clearly suggests that those who have experienced victimization, or know someone who has, are more likely to perceive a higher subjective probability of victimization (Ferraro, 1995; LaGrange & Farraro, 1989; Smith & Hill, 1991; Weinrath & Gartrell, 1996). As mentioned previously, the victimization experience is rare for most individuals, and most are exposed to victimization through their friends, relatives, and social networks (Mesch, 2000). Further, research suggests that individuals acquainted with significant others who have been victims of crime have a higher perception of risk (Mesch, 2000).

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Figure 2: Super controllers and crime (Sampson, Eck & Dunham 2010).

Important to note are the contentions of Sampson et al. (2010) who expanded routine activity theory to include the necessity of so-called controllers and super-controllers for each of the respective elements necessary for a crime to occur. As shown in Figure 1, Sampson et al (2010) argued that offenders, targets, and guardians are moderated directly by controllers and indirectly by super-controllers who moderate the controllers. For example, college campuses employ security to guard against offenses against suitable targets by motivated offenders. The heads of these departments would be considered controllers who are controlled then by the governing board of the institution and state and federal lawmakers who, by extension, have indirect control over the absence of either motivated offenders, suitable targets, or capable guardians through policy and procedures which directly affect controllers (Sampson et al., 2010). The role of media as a diffuse super-controller is important to consider in this regard. Media reports, whether they are received via social media, print, television, or radio are diffuse because they are not formal super controllers and can influence other diffuse super controllers, as

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well as formal and personal super controllers (Sampson et al., 2010).7 For example, a news report of a rape may go out on local news, be picked up by a local police department’s social media manager, distributed via the department’s page to the all subscribers within the community, and re-shared by members of the community. Consequently, the behavior of guardians (security guards, etc.) and motivated offenders who are reached by such a report (or reports of police response to the original report) could alter their behavior, while the precautionary or risky behaviors of suitable targets (students) could also be augmented in response to the report. Further, perceived safety and fear of crime could arguably be affected if the report was in another city or town, but the target was repeatedly exposed to the story or commentary about it via their Facebook news feed. Precautionary behaviors With respect to the measurement of the guardianship dimension of routine activity theory, the contention among criminologists is that precautionary behaviors are variable across sociodemographic categories and differential perceptions of risk (Rountree & Land, 1996a). Guardianship activities, as outlined by routine activity theory, can include strategies of avoidance and defensive behavior on the part of suitable targets (Ferraro, 1995). Avoidance behaviors can include avoiding certain locations known to be high-crime or unsafe, and defensive behaviors can range from using extra locks to keeping firearms for protection. Certain lifestyle behaviors such as going out with friends, binge drinking, recreational drug use, and others are prevalent among college students (SAMHSA, 2014a; SAHMSA, 2014b), and research has shown that

7

Diffuse super controllers are referred to as media, political actors and markets, which differ from formal super controllers (e.g., courts, regulatory agencies) and personal super controllers (e.g., family, groups) in that they are not necessarily concentrated or intent on altering or incentivizing specific controller behavior.

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these lifestyle behaviors are predictive of the use of guardianship behaviors, and that as feelings of safety decrease, precautionary behavior increases (Riger & Gordon, 1981; Rountree & Land, 1996a; Rountree & Land, 1996b; Tewksbury, & Mustaine, 2003). Although some findings have been inconsistent regarding the reciprocal relationship between fear of crime and the use of precautionary behaviors (Rountree, 1998), research has shown that those who incorporate precautionary behaviors are generally more afraid of potential victimization than those who do not (Ferraro, 1996; Fisher & Sloan, 2003; Rountree, 1998). Hypotheses Both routine activity theory and cultivation theory are most appropriate for the present study because the overall framework can be used to explain not only the influence of media consumption variables such as time spent watching television, reading print news, or checking social media, but also the outcome variables through measurement of fear of crime, perceived risk of victimization and constrained behavior. Further, researchers have employed elements of cultivation and routine activities theory to investigate perceived risk of victimization and attitudes toward crime in general (Cohen & Felson, 1979; Iyengar & Kinder, 1987; McCombs, Lopez-Escobar, & Llamas, 2000; Mesch, 2000; Romer et al., 2003). The same logic by which cultivation processes are found to occur in television media is arguably viable when questioning the influence of exposure to images and stories viewed by Facebook users, and perhaps even more so when considering the extent to which stories may involve the victimization of members of users’ social networks. The present study builds on existing media effects and criminological research by analyzing the correlations between Facebook use, traditional media use, political views, sociodemographic characteristics, prior victimization, perceived community cohesion,

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precautionary behaviors, drug and alcohol use, and fear of crime and perceived risk. This study builds upon extant research by using a sample of college students attending non-residential community college and university campuses in the Pacific Northwest, analyzing victim-offender relationships, and improving measurement by using cognitive, emotional, and behavioral variables relating to both fear of crime and Facebook use. The question the proposed study seeks to investigate is whether Facebook usage positively or negatively influences fear of crime and perceived risk of victimization among liberal and conservative college students attending nonresidential campuses when all other factors are held constant. Based on prior research and theory development, the following relationships between control variables and the dependent variables of interest will be examined. First, in terms of race, non-whites will be expected to report a greater fear of crime and perceived risk than whites (Rountree & Land, 1996a, 1996b; Rountree, 1998; Reid & Konrad, 2004). Women will also be expected to report greater levels of fear of crime, perceived risk, and use of precautionary behaviors (Ferraro, 1995; Gordon, Riger, LeBailly, & Heath, 1980; Stanko, 1990). Whether respondents have experienced prior direct or indirect victimization is expected to be associated with greater levels of fear of crime, perceived risk, and precautionary behaviors (Ferraro, 1995; LaGrange & Ferraro, 1989; Smith & Hill, 1991; Weinrath & Gartrell, 1996). Additionally, respondents living in a house will be expected to report greater levels of fear of crime, as will those who report living with roommates other than their parents (Hale, 1994). Respondents who report greater levels of traditional media usage are also expected to report greater fear of crime, perceived risk of victimization and use of precautionary behaviors (Baker, Niendstedt, Everett, & McCleary, 1983; Ferraro, 1995; Slovic et al., 1980, 1982; Warr, 2000). In terms of community characteristics, respondents reporting greater levels of perceived community cohesion are

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expected to report lower levels of fear of crime and perceived risk of victimization (Lee & Earnest, 2003; Sampson, Raudenbush, & Earls, 1997). Because prior research has shown differences in news consumption and perspectives on crime, punishment and perceived risk across the political spectrum (Garrett et al., 2013; Stroud, 2011; Unnever et al., 2007), and given the potential for both the echo chamber and filter bubble effects on Facebook use (Pariser, 2011), political ideology will be an independent variable of interest. This will be examined in relation to intensity of Facebook usage and the two variables’ predictive power on response measures of fear of crime, perceived risk of victimization, and use of precautionary behaviors, over and above all other factors. Thus, the following hypotheses will be tested: H1: Conservatism will be positively associated with fear of crime, perceived risk of victimization, and use of precautionary behaviors, all other factors held constant. H1a: Fear of crime, perceived risk of victimization, and use of precautionary behaviors will be will be greater among females, all other factors held constant. H2: Intensity of Facebook use will be significantly associated with increased levels of fear of crime, perceived risk of victimization, and use of precautionary behaviors among conservatives, all other factors held constant. H3: Greater levels of perceived community cohesion will be significantly associated with decreased levels of fear of crime, perceived risk of victimization, and use of precautionary behaviors, all other factors held constant. H4: Intensity of Facebook use will not be significantly associated with greater levels of perceived community cohesion.

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CHAPTER THREE: METHODOLOGY Data Data was collected during the fall sessions of three non-residential college campuses in the Pacific Northwest. Appropriate staff were contacted at a total of four institutions, with three responding. IRB applications were submitted to each institution and each designated a staff member to coordinate the distribution of the survey once approval to conduct the study was granted.8 Surveys were sent via email to a random sample of all registered students from two of the participating institutions, and an anonymous survey link was made available to registered students on the home page of the third institution’s learning management system (LMS). Although a precise overall response rate could not be calculated for each institution, given the fact that some students may have had emails automatically sent to spam folders, and that students enrolled in classes at the second institution may not have logged on to their LMS, a total response rate was calculated based the number of students who responded minus the total number of those contacted or exposed to the survey. Overall, a total of 3,689 students had an opportunity to complete the survey, with 425 responding, for a total response rate of 11.62 %. Of these responses, 54 were incomplete or unusable, for a total sample of 371 valid responses.9 The results of an a priori power analysis for a linear multiple regression: fixed model, R2 increase using the G*Power software program suggests that a sample of 119 should be obtained for a medium effect (.95 at α=.05) given the number of independent variables in the design.

8

The survey was created and distributed using the Qualtrics platform. Some respondents did not meet the minimum age requirement for participation and their responses were subsequently excluded from the analyses. 9

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The survey was anonymous, and participants were given the option to enter a sweepstakes to win a $100 gift card for their participation. Participants entered the sweepstakes by providing email addresses. Email addresses were collected by redirecting participants to a second survey to maintain anonymity. Emails sent to the first two institutions contained a minor assent statement, as did the first page of the online survey (See Appendices A and B for the survey and minor assent disclosure). The survey included 33 questions about respondents’ sociodemographic characteristics, including age, race/ethnicity and gender. The survey also included questions related to respondents’ use of precautionary behaviors, fear of crime, perceived risk of victimization, leisure activities, drug and alcohol use, media use, prior victimization, type of residence, living situation, SNS use, and political leanings. Measures Dependent Variables The dependent variables included in the present study are fear of crime, perceived risk, and use of precautionary behaviors. Dimensions of the fear of crime variable were measured using the Fear of Crime in America Survey, which measures respondents’ general fear of crimes ranging from fraud to murder (Ferraro, 1995; Ferraro & LaGrange, 1992). However, three crimes were added to the survey based on more recent research dealing with the measurement of fear of crime: a) physical assault, b) domestic violence, and c) stalking (Madriz, 1997; Truman, 2007).10 Also, a distinction between stranger and non-stranger have been made for these three crimes based on previous literature dealing with these points (Fisher & Sloan, 2003; Pain, 1995; Truman, 2007; Wilcox et al., 2006). Respondents rated their fear of each item from (1) not afraid

10

See Appendix C for a complete list of these items and the results of the univariate and bivariate analyses discussed.

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at all to (10) very afraid. An overall fear index was created with a theoretical and actual range of perceived risk of victimization to specific crimes were measured per the same Likert-scale rating as the fear of crime measure. These measures were also based off the Fear of Crime in America Survey (Ferraro, 1995; Ferraro & LaGrange, 1992). Precautionary behaviors were measured using a section of the Fear of Crime in America Survey (Ferraro, 1995; Ferraro & LaGrange, 1992), though some additions were taken from the Fear of Rape Project (Gordon & Riger, 1991) based on previous literature (Gordon & Riger, 1991; Truman, 2007). Just as with the fear of crime and perceived risk measures, appropriate indexes with appropriate theoretical ranges were constructed for precautionary behaviors using SPSS. Independent Variables A total of (11) independent variables were included in the study: Facebook usage, traditional media usage, political ideology, perceived community cohesion, risk-taking behavior, gender, race/ethnicity, type of residence, living situation, prior victimization and indirect victimization. The first independent variable of interest was intensity of Facebook usage, which was measured along two dimensions: frequency and duration. Frequency was measured by asking respondents about how often they used Facebook as a source of news from (1) never to (5) every day; duration was measured using a portion of the Facebook Intensity Survey, which asked how much time they spend on Facebook per day, from (1) less than 10 minutes to (6) more than 3 hours (Valenzuela, Park & Kee, 2009). In line with the current literature dealing with the measurement of SMS use (Li, Lau, Mo, Su, Wu, Tang & Qin, 2016), a reliability analysis of these measures produced an acceptable Cronbach’s alpha value of α=.670. In addition, respondents were asked whether they follow their local police department on Facebook, and the

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three variables were added to create an overall Facebook intensity score with a range of 0 to 11 — with higher scores indicating more intense usage of the SNS. The second independent variable of interest was political ideology, which was measured according to the degree to which participants consider themselves liberal, moderate or conservative, with scores ranging from (1) very liberal to (5) very conservative. Finally, respondents were asked to report whether they felt their neighbors would help if there was a problem and whether they felt as though they were a part of their community. These variables were added to create an overall measure of respondents’ perceived community cohesion. Scores ranged from (0) to (3), with a higher score indicating greater levels of perceived community cohesion. Control variables Mass media use was measured according to the frequency with which respondents reported consuming national television news, local television news, radio news programs, news magazines, daily local newspapers, national daily newspapers, and news websites, with scores ranging from (1) never to (5) every day (Truman, 2005). An overall media score was created to measure respondents’ overall use of traditional media with a range of 7 to 27, with higher scores indicating greater levels of traditional media consumption. To measure key demographic variables, respondents were asked to indicate whether they consider themselves to be male, female, or neither male nor female. This variable was dummy coded for multivariate analysis as (0) male or other and (1) female as the reference category. Respondents were asked to identify themselves as (1) White/Caucasian, (2) African American/Black, (3) Hispanic/Latino, (4) Asian, (5) American Indian or Pacific Islander, or (6) other. These were dummy coded for multivariate analysis as (0) white, with (1) nonwhite as the

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reference category. Respondents were asked to report the type of residence as (1) apartment, (2) house or (3) other, and this variable was dummy coded for multivariate analysis with (1) house as the reference category. Respondents were also asked to report their living situation as (1) live alone, (2) live with roommates (spouse, friends, etc.), (3) live with parents, or (4) other. Very few respondents reported living alone, these items were dummy coded for multivariate analysis with (1) live with roommates other than parents as the reference category. With respect to the leisure activities measures, respondents were asked to rate how often they socialize or party with friends and socialize or party with strangers on a scale of (1) never, to (6) daily or almost daily (Mustaine & Tewksbury, 2002; Rountree, 1998; Tewksbury & Mustaine, 2003; Truman, 2007). To measure risk taking behavior, respondents identified on a scale of (0) never to (6) daily or almost daily how often in the previous year they had enough alcohol to get drunk, smoked marijuana or hashish, or used other illicit drugs (modified from Fisher, Sloan & Cullen, 1999; see also Truman, 2007). An index was then created to measure respondents’ overall engagement in risk-taking behaviors, with a range of 3 to 22, with a higher score indicating greater levels of risk-taking behaviors. To measure prior direct or indirect victimization among respondents, respondents were asked whether they had been the victim of crimes ranging from property crime to sexual violence by answering (1) yes and (2) no (Fisher et al., 2000; Fisher & Sloan, 2003; Truman, 2007). Respondents who indicated they were victims of these crimes indicated (1) yes or (2) no as to whether the victimization took place within the past year and (1) yes or (2) no as to whether the offender was known to them. An overall victimization index was recoded for multivariate analysis with a range of 0 to 3, with a higher score (0=nonvictim, 1=property crime victim, 2=property and nonsexual violence victim, and 3=property crime, nonsexual violence, and

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sexual violence victim) indicating more instances and severity of victimization. Finally, respondents indicated whether a close friend or relative had been the victim of a crime in the past year, which was later recoded with (1) yes as the reference category. Analytic Strategy Analyses of the data collected were conducted at the univariate, bivariate and multivariate levels. The following frequency distributions are provided for measures of respondents’ Facebook use intensity, sociodemographic characteristics, types of residence, living situation, risk-taking behaviors and leisure activities, perceived community cohesion, prior and indirect victimization, traditional media usage, political ideology, fear of crime, perceived risk of victimization and use of precautionary behaviors. To examine the associations between the independent variables and overall measures of fear of crime, perceived risk and precautionary behaviors, Pearson correlation coefficients are reported. Finally, hierarchical linear regressions are used to predict fear of crime, perceived risk of victimization and use of precautionary behaviors.

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CHAPTER FOUR: ANALYSIS Univariate Analysis Sociodemographic Characteristics The sample obtained includes a total of 371 students from three separate non-residential colleges and universities in Washington State. Table 1 shows frequency distributions of respondents’ sociodemographic characteristics, mass media use, political ideology, prior victimization, risk-taking behaviors, and perceived community cohesion. In terms of sociodemographic characteristics, approximately 21% of respondents reported their gender as being male, approximately 78% were reportedly female, and approximately 1% reported identifying as neither male nor female. Respondents’ mean age was approximately 26. More than 70% of respondents reported their race/ethnicity as white, while approximately 2% identified as black, 7% as Hispanic/Latino, 4% as Asian, 3% as American Indian or Pacific Islander, and 5% as other. Approximately 20% of respondents reported living in an apartment, and roughly 80% reported living in a house or other dwelling. Only 5% of respondents reported living alone, while roughly 55% reported living with roommates, 34% reported living with their parents, and 6% reported another type of living situation that did not fall into these categories. In terms of reported political ideology, scores were normally distributed across cases and the average respondent identified as moderate, though the sample was slightly skewed toward liberalism over conservatism and had mean score of 2.86 (N=286). Respondents’ use of traditional media was evenly distributed across categories, apart from news magazines, national daily papers, and daily local papers, which were the most negatively skewed categories. Respondents were asked how often they used each medium for news consumption, with (1) meaning never and (6) meaning daily or almost daily. The most

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commonly frequented outlets were local T.V. news and news-based websites, which had mean scores of 2.64 (N=335) and 3.1 (N=336), respectively. Behind these, respondents frequented radio news programs and national T.V. news the most, with mean scores of 2.57 (N=334) and 2.41 (N=335), respectively. An overall media consumption index produced an actual range of (0) to (27), and was normally distributed among cases, with a mean score of 15.78 (N=331). Overall, respondents preferred local television and web-based news over other mediums. Of 371 valid responses, approximately 93% of respondents reported having a Facebook account and only 7% reported having a Nextdoor account. Of those respondents reporting a Nextdoor account, approximately half reported receiving email updates from the app. As such, these items were excluded from further analyses and only Facebook measures were included. Of the 93% of respondents who reported having a Facebook account, 36% reported following their local police department’s Facebook page (N=307). Recoding the same measure of frequency of using Facebook as a news source as traditional media measure, mean frequency of Facebook use was positively skewed with a mean of 2.36 (N=335). The mean score for duration of Facebook use was 2.75. In other words, respondents spent an average of 10-30 minutes a day on Facebook. As shown in Table 1, overall intensity of Facebook usage scores ranged from (0) to (11), and values were normally distributed among cases with a mean score of 5.19. Distributions of victim scores among females and those identifying as male or neither male nor female differ greatly in terms of the type of crime and whether the offender was known to the victim. Approximately 54% of females reported being a victim of property crime compared to 62% of those identifying as male or neither male or female. Of those, 28% of females and 22% of males reported that the victimization had occurred in the past year, and 34% of females reported the offender was known to them, compared to just 16% of males and those

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identifying as neither male nor female victims. Overall property crime-victimization scores ranged from (0) non-victim to (3) victim within the past year and offender known. The mean score for females was similar to that of males and those identifying as neither male nor female, at .85 and .81, respectively. With respect to nonsexual violence or physical assault, non-females also differed to some extent than females, with a higher percentage of females reporting victimization. Thirty-four percent of females reported being victims of nonsexual violence, compared to 22% of non-females. While a higher percentage of males or those identifying as neither male nor female reported victimizations occurring in the past year (24%), 90% of the respondents reporting that they knew the offender were female (90%), compared to 72% of nonfemales reporting the same. The overall victim scores for nonsexual violence saw a mean value of .43 for non-females and .70 for females. Intuitively, and in line with prior research, more females (N=100) reported being victims of sexual violence than non-females. Thirty-nine percent of females reported being victims of sexual violence, compared to 10% of non-females (N=8). Of those, a greater number of nonfemales reported the victimization occurring within the past year (38%) and a greater percentage of females reported that the offender was known to the (89%), though the percentage of nonfemales reporting knowing the offender was almost the same at 88%. The overall victim scores for sexual violence saw a mean value of .22 for males and .81 for females. The mean values for the total victim index were negatively skewed among both males and females, though mean values were higher for females than non-females at 2.34 and 1.40, respectively. With respect to indirect victimization, responses were evenly split among respondents, with 48% reporting that a close friend or relative had been the victim of a crime.

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Individual items measuring respondents’ risk-taking behaviors and leisure activities ranged from (1) never to (6) daily or almost daily. The modal category for socializing and partying with friends was (5) once or twice a week, while the modal category for respondents socializing or partying with strangers was (1) never among all respondents (N=327). This was also true for respondents when asked how often they had had enough alcohol to get drunk, had smoked or eaten marijuana products, or used illicit drugs such as cocaine or LSD. Overall risktaking behaviors scores ranged from (3) to (22), with higher scores indicating more risk-taking behaviors. This was normally distributed, with a mean value of 11.42 (N=330). Table 1 presents frequency distributions for items measuring perceived community cohesion among all respondents. Generally, a higher percentage (54%) of respondents reported that they felt they could rely on their neighbors for help than those who did not, though a lower percentage (44%) reported feeling like they were a part of their community than those who did not. Overall perceived community cohesion ranged from (0) to (2), with higher scores indicating greater levels of perceived community cohesion. Overall scores were positively skewed, and the mean value was 1.23 for all respondents (N=293).

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Table 1: Frequency Distributions for Sociodemographic Characteristics, Media Use, and Facebook Use (n = 371) Variable Gender Male Female Neither Male or Female

Age Race White Black Hispanic/Latino Asian American Indian or Pacific Islander Other

Type of Residence Apartment House Other

Live With Live Alone Live with Roommates Live with Parents Other

Mass Media Use Measures (Overall) National T.V. News Local T.V. News Radio News Program News Magazines Daily Local Newspapers National Daily Newspapers News-Based Websites

Political Ideology (5=Very Conservative) Facebook Use Measures (Overall) Has Facebook (1=Yes) Follows police department page (1=Yes) Facebook Use for News Duration of Facebook Use

Victim Measures Property crime (overall) Victim (1=Yes) Victimization in past year (1=Yes) Offender known (1=Yes)

Nonsexual violence (Overall) Victim (1=Yes) Victimization in past year (1=Yes) Offender known (1=Yes)

Sexual violence (Overall) Victim (1=Yes) Victimization in past year (1=Yes) Offender known (1=Yes)

Total Victim Score (Overall)a Indirect Victim (1=Yes) Risk-Taking Behaviors Measures Socializing with friends (past year) Socializing with strangers (past year) Had enough alcohol to get drunk (past year) Smoked or eaten marijuana (past year)

Perceived Community Cohesion Measures Rely on neighbors for help (1=Yes) Feel a part of your neighborhood (1=Yes)

mean

SD

----------------25.86 ----------------------------1.84 ------------2.41 ----------------15.78 2.41 2.64 2.57 1.63 2.01 1.69 3.10 2.84 5.19 1.93 .37 3.36 2.75

----------------9.305 ----------------------------.466 ------------.687 ----------------4.787 1.195 1.230 1.280 .873 1.04 .937 1.357 1.218 2.812 .253 .483 1.426 1.386

.84 ------------.64 ------------.67 ------------2.11 .48 11.42 4.02 2.01 2.49 2.18 1.23 .78 .66

.900 ------------.978 ------------.988 ------------2.042 .500 3.882 1.231 1.270 1.430 1.780 .822 .412 .474

Sample sizes vary for select variables due to missing cases.

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Total 21.4% 77.7% .8%

78.4% 1.8% 7.0% 3.6% 3.3% 5.1% 19.9% 75.9% 4.2% 5.1% 54.7% 33.9% 6.3% ----------------------------------------93.1% 36.8% ------------31.3% 19.3% 4.8% 3.9% 21.3% 4.3% 4.7% 24.3% 4.4% 39.1% ------------------------53.9% 43.9%

Range 1-3 ------------18-60 1-6 ------------------------1-3 ------------1-4 ----------------1-6 ----------------------------1-5 1-11 --------1-6 1-6 0-3 ------------0-3 ------------0-3 ------------0-9 1-6 ----------------0-2 ---------

Bivariate Analysis Fear of Crime and Perceived Risk of Victimization Support for the hypothesis that females will express greater levels of fear of crime than non-females was found at the bivariate level. Pearson correlation coefficients, means and standard deviations were calculated for individual items measuring fear and perceived risk of fifteen specific crimes, ranging from being murdered to having one’s car stolen (See Appendix C for a complete table of these results). As mentioned above, these crimes were organized according to their nature into the following categories: property crime, nonsexual violence and sexual violence. Table 2 shows Pearson correlation coefficients calculated for the total scores calculated for respondents’ fear perceived risk, as well as the total number of precautionary behaviors used. In line with previous research on fear of crime and perceived risk (Farraro, 1995; Warr & Stafford, 1981), significant, positive linear relationships are found between overall and individual item-measures of fear and perceived risk, though the strength of select relationships vary across genders, depending on the nature of the crime in question.11 Both fear and perceived risk of victimization were measured using a 10-point scale, with (1) being not afraid at all to (10) being very afraid, and (1) being not likely at all and (10) being very likely, respectively. Significant, strong correlations were found between measures of respondents’ fear and perceived risk of property crime and both nonsexual and sexually violent crimes, and when examining these relationships among genders, the relationship between fear and perceived risk of being raped or sexually assaulted by a stranger was moderate to strong among both non-female and female respondents (r=.478, p