Investigating the Influence of Social Media Consumption on Punitive Attitudes Among a Sample of U.S. University Students

Abstract

Prior research suggests that media consumption influences attitudes toward punitiveness. Traditionally, prior efforts have generally examined television news and crime-related programming. However, less is known whether more contemporary forms of media consumption, such as social media, are related to punitive attitudes. Using a multisite sample of more than 900 mostly young adults, the current study examines (a) the relationship between four types of social media consumption (overall, general news, crime-specific content, and punishment-specific content) on penal attitudes and (b) whether these relationships vary based on key characteristics. Results reveal that individuals who consume punishment-specific content on social media are significantly more likely to have stronger attitudes regarding the use of punishment and this relationship varies by fear of crime. Findings and directions for future research are discussed.

Keywords

social media punishment college students audience effects attitudes

Introduction

The United States has the highest total prison population in the world and the highest incarceration rate among developed countries (Walmsley, 2016). Although the later part of the 20th century showed public attitudes and policies favored more severe punishment practices (Cullen, Fisher, & Applegate, 2000), recent public opinion polls illustrate a trend favoring rehabilitative services and reducing harsh sentencing policies (Pew Charitable Trusts, 2016; RTI International, 2017). Although public attitudes are shifting toward less penal responses, some surveys still show that Americans prefer more punitive actions. For example, the 2017 Gallup Poll illustrates that more than half of Americans are in still favor of the death penalty for convicted murderers (55%; Jones, 2017). In a similar vein, the 2016 General Social Survey (GSS) shows that majority of Americans (56%) still believe the courts are not harsh enough when dealing with criminals. When discussing high rates of incarceration and strict punishments, some scholars suggest that public opinion shapes punitive policies (Enns, 2014; Tonry, 2004), whereas others believe the relationship between public opinion and crime-related policy is less straightforward (Frost, 2010). Yet, recent evidence suggests that shifts in public penal attitudes may be influenced by political actors and how they frame the issue of crime in society (Ramirez, 2013).

To date, scholars have identified several correlates that predict punitive attitudes, which include, but not limited to sociodemographic characteristics (e.g., age, race/ethnicity, sex, political ideology, education, income, religion), perceptions of crime and fear of victimization (Applegate, Cullen, & Fisher, 2002; Bader, Desmond, Mencken, & Johnson, 2010; Cochran & Chamlin, 2006; Costelloe, Chiricos, & Gertz, 2009; Dowler, 2003; Johnson, 2007; Spiranovic, Roberts, & Indermaur, 2012). Yet, a small but growing body of research illustrates that media consumption is associated with punitive attitudes (Garofalo, 1981; Goidel, Freeman, & Procopio, 2006; Kort-Butler & Hartshorn, 2011; Rosenberger & Callanan, 2011; Surette, 2007). Indeed, prior efforts suggest that various types of media and news consumption, such as watching television news (local and national broadcasts) and crime-related programming, influence attitudes related to punitiveness (Gilliam & Iyengar, 2000; Goidel et al., 2006; Kort-Butler & Hartshorn, 2011; Roche, Pickett, & Gertz, 2016; Rosenberger & Callanan, 2011; for an exception, see Dowler, 2003). Although previous studies have contributed significantly to the media consumption and punitiveness relationship, a specific limitation in this area of research is the impact of social media consumption on individuals’ attitudes toward punitiveness. This oversight in the literature is unexpected given that communication scholars suggest that new forms of media, such as social media, are important platforms “in which news and information are disseminated among the public” (Cummings & Gottshall, 2014, p. 615; see also Morgan & Shanahan, 2010). Subsequently, the content consumed may affect individuals’ beliefs and attitudes regarding topics related to crime and justice (Dowler, 2003; Gross & Aday, 2003).¹

Understanding whether social media consumption is associated with punitive attitudes is important for several reasons. First, recent reports illustrate that not only the number of individuals who utilize social media platforms has extensively grown from 5% of U.S. adults using social media in 2005 to 69% of U.S. adults in 2016 (Pew Research Center, 2017), but approximately two thirds (67%) of Americans receive news from social media sites (Shearer & Gottfried, 2017). Thus, social networking sites are increasingly becoming a major platform by which individuals turn to—and rely on—for content and information. Second, social media sites provide many unique characteristics over traditionally based media platforms (e.g., television, newspaper, radio). Social media users can access news and information shared by others, engage in conversations (e.g., via commenting on posts and stories), and search/filter for content. Moreover, by serving as a communication tool to others, social media allows users to participate in a discussion on virtually any subject/issue, which in turn, may alter or change one’s opinion or attitude about the topic of discussion (A. A. Anderson, Brossard, Scheufele, Xenos, & Ladwig, 2014). For example, a recent survey found that approximately 20% of social media users have changed their stance on a social or political issue due to the content they consumed from social media (M. Anderson, 2016). Third, media platforms such as broadcast news and entertainment television shows have been shown to heavily focus on crime- and justice-related content to increase viewers’ interest, obtain higher viewer ratings, and satisfy the demand for action news (Gilliam & Iyengar, 2000; McCall, 2007). Relatedly, some scholars suggest that media consumption may alter or shape public opinion about criminal justice policies (Beale, 2006; Surette, 2007). In turn, public opinion about the criminal justice system has important implications for influencing the development, operation, and success of such policies aimed at corrections and reentry (Enns, 2014; Garland, Wodahl, & Schuhmann, 2013; Pickett, Mancini, Mears, & Gertz, 2015). Thus, it is salient to understand whether and how the consumption of a major media platform, such as social media, may exacerbate attitudes directed toward punitiveness that can potentially influence policy and practices in the criminal justice system.

The present investigation’s aim is to contribute and advance the literature on media consumption and attitudes toward punitiveness (and crime and justice more generally) in two important ways. First, it extends the cultivation framework by examining whether social media consumption is related to individuals’ attitudes toward punishment. Specifically, the study assesses four types of social media consumption, which include overall consumption, general news consumption, specific consumption of news/stories involving crime and violence, and specific consumption of news/stories involving the sentencing or punishment of criminals, controlling for major mediums of communication and information (e.g., television news, the Internet, and crime-related television programming), demographics, and correlates of punitiveness. Second, it disaggregates the sample based on key audience characteristics and correlates—race, gender, political ideology, residential location, prior victimization, and fear of crime—to examine whether the relationship between social media consumption and punitiveness is prominent among specific subgroups. Surprisingly, with the exception of a single study (Roche et al., 2016), no other research has examined whether the relationship between media consumption and punitive attitudes varies by audience characteristics.

Before turning to the results, the theoretical rationale for this study, grounded in the cultivation framework, is outlined. From there, correlates of penal attitudes and previous literature on the relationship between media consumption and punitiveness are discussed.

Theoretical Background

Developed by George Gerbner and his colleagues in the 1960s, cultivation theory contended that mass media, specifically television consumption, distorted how consumers viewed social reality (Gerbner, 1969; Gerbner & Gross, 1976). Stated differently, individuals who consume more media will hold views, attitudes, and beliefs that are consistent with what is portrayed in the media. Concerned with the amount of violence portrayed on television programming and its long-term consequences on consumers, Gerbner and colleagues found support that heavy television viewership cultivated fear among television consumers, ultimately perceiving the world as a mean place (Gerbner et al., 1977; Gerbner, Gross, Morgan, & Signoorielli, 1980). However, because of several criticisms surrounding the cultivation framework’s predictive power as well as its homogeneous effects assumed across all consumers (Doob & MacDonald, 1979; Hirsch, 1980; Hughes, 1980), researchers, including Gerbner, begun to account for the variation among consumers’ characteristics (e.g., demographics, backgrounds, and past experiences) in the cultivation process.

Broadly referred to as “reception research” or “audience reception theory,” there are four key perspectives that account for the differences in media users’ characteristics that may influence attitudes and perceptions related to crime and the criminal justice system. The mainstreaming perspective illustrates that, regardless of group differences, heavy media consumption homogenizes individuals to share similar views (Gerbner et al., 1980; Morgan, Shanahan, & Signorielli, 2014). From this perspective, the media’s influence on punitiveness would be uniform across consumers’ demographic characteristics and social backgrounds. The substitution hypothesis suggests that media effects may be more pronounced among consumers without personal experiences with crime and the criminal justice system (Gunter, 1987; Liska & Baccaglini, 1990). From this point of view, the media’s influence on punitiveness may be more pronounced among minorities, females, those with lower emotional responses to fear, and among individual/background characteristics that are generally less punitive in nature such as those who live outside of the Southern region of the United States and those who identify as more liberal in political affiliation.

In contrast, the resonance hypothesis suggests that cultivation effects will be amplified among individuals when the media content resonates with their own experiences or beliefs (Doob & Macdonald, 1979; Gerbner et al., 1980). From this viewpoint, the media’s influence on punitiveness may be more pronounced among individual/background characteristics that are generally found to have more punitive attitudes such as Whites, males, religiosity; those who identify as more conservative; and those who reside in the Southern region of the United States. The vulnerability perspective contends that media messages will be more prominent among individuals who are vulnerable or susceptible to crime and violence (Skogan & Maxfield, 1981). From this standpoint, the media’s influence on punitiveness may be more pronounced among crime victims, those who have higher emotional responses to fear, and correlates of crime/violence such as individuals who have lower incomes and educational attainment.

Overall, theoretical accounts of media effects suggest that greater consumption of media may affect individuals’ attitudes related to crime and justice. Furthermore, these effects may vary based on consumer’s characteristics such as demographics, past experiences, and social backgrounds. Before turning to the previous research on media consumption and punitive attitudes, the correlates of punitiveness are discussed.

Characteristics Associated With Punitiveness

Scholars contend that public opinion shapes criminal justice policy. For example, the “get tough” on crime policies and the rise of mass incarceration is believed to reflect the public’s support for tougher punishments (Cullen et al., 2000; Wozniak, 2016). Debates ranging from the war on drugs to illegal immigration to gun control illustrate the variety of interests surrounding crime-control policies among the public. Perhaps, one of the most studied policies regarding the criminal justice system can be found with capital punishment. Public opinion polls, such as the GSS and Gallup poll, have produced data on whether Americans support the death penalty over the past 50 years. For example, a recent study has examined the trends with public opinion on the death penalty and found that Blacks (compared with non-Blacks) have decreased their support for capital punishment over time (Shirley & Gelman, 2015). Furthermore, with respect to age and education, 18- to 29-year-olds and those with higher education (e.g., graduate degree) have been shown to be less likely to support the death penalty.

To better understand the characteristics associated with punitive attitudes, researchers have examined a host of factors associated with public attitudes on punishment, which often include demographic characteristics and perceptions/experiences with crime and justice. With respect to demographic factors such as sex and race/ethnicity being associated with punitive attitudes, prior efforts suggest that males and Whites are generally more likely than females and non-Whites to be more punitive and/or support harsher punishment (Applegate et al., 2002; Cochran & Chamlin, 2006; Evans & Adams, 2003; Johnson, 2007; Roberts & Indermaur, 2007).² Regarding the association between age and punitiveness, research tends to show inconsistent results (Cullen, Clark, Cullen, & Mathers, 1985; Rosenberger & Callanan, 2011; Van Kesteren, 2009), where some contend that the age and punitive relationship is curvilinear (Franklin & Franklin, 2009). Other demographics, such as income, educational level, political ideology, and religion have also shown to be related to penal attitudes. For instance, findings tend to illustrate that individuals with lower income, less education; those who are conservative; and those who are more religious support harsher punishment strategies (Applegate, Cullen, Fisher, & Vander Ven, 2000; Cochran & Chamlin, 2006; Costelloe et al., 2009; Dowler, 2003; McCorkle, 1993; Roberts & Indermaur, 2007; Unnever, Cullen, & Applegate, 2005; for exceptions, see Kury & Ferdinand, 1999; Rosenberger & Callanan, 2011).

A second area of research that focuses on measures related to punitive attitudes is indicators of crime salience, which often consists of perceived or relevant experiences with crime and the criminal justice system (e.g., fear of crime, perceptions of crime issues, and victimization; Frost, 2010; see also, Costelloe et al., 2009). As stated by Costelloe and colleagues (2009), “People for whom crime is more problematic issue might be reasonably expected to endorse tougher measures to deal with it” (p. 27). Consistent with this statement, sentiments related to crime are associated with punitiveness. In fact, prior efforts suggest that fear of crime is related to punitive attitudes (Applegate et al., 2000; Armborst, 2017; Costelloe et al., 2002, 2009; Dowler, 2003; Johnson, 2009; for lack of support, see Kleck & Jackson, 2017). Although measured in various ways, additional emotional responses to crime, such as anger and concern about crime, have also been shown to be linked to more punitive responses (Johnson, 2009; Roberts & Indermaur, 2007; Spiranovic et al., 2012). Finally, actual experiences with crime, such as victimization, has also been examined with punishment attitudes. Surprisingly, previous efforts show that prior victimization is not generally related to punitiveness (Applegate et al., 2000; Rosenberger & Callanan, 2011); however, the perceived probability of being a victim has been shown to be associated with harsher responses to punishment (Costelloe et al., 2002).

A third line of work has looked at racial typification and increased support for punishment. Recognizing that mass media coverage disproportionately highlights minority criminals as well as nonminority victims (Chiricos & Eschholz, 2002; Dixon & Linz, 2000), scholars have been fascinated in examining whether individuals who typify crime as a vastly Black occurrence are more punitive in nature. Drawing from the arguments embedded in racial/social threat theory (Blalock, 1967; Liska, 1992), prior efforts illustrate that individuals who typify Blacks as criminal threats are more likely to support harsher criminal punishments and policies (Barkan & Cohn, 2005; Chiricos, Welch, & Gertz, 2004; King & Wheelock, 2007). Similar findings have been echoed among studies that examined the racial typification of Hispanic offenders and support for punishment and crime-control policies (see Rocha & Espino, 2009; Welch, Payne, Chiricos, & Gertz, 2011). And finally, studies highlight that racial typifications about youth criminality are also associated with more punitive attitudes toward juvenile offenders (Metcalfe, Pickett, & Mancini, 2015; Pickett & Chiricos, 2012).

In summary, prior efforts have linked various demographics and characteristics to punitive attitudes; yet, a different, but growing body of research suggests that consumption of news and entertainment media may be associated with punitiveness. Research in this domain is discussed next.

Media Consumption and Punitive Attitudes

Mass media is a primary source for crime- and justice-related stories and events. In fact, scholars contend that individuals not only receive most of their knowledge about crime and the criminal justice system from the media but their attitudes related to crime and justice are also influenced by what is consumed from media (Gross & Aday, 2003; Surette, 2007). In addition, research suggests that mass media coverage on crime and violence may increase public support for punitive policies (Beale, 2006; Frost, 2010). Although there is a lack of consensus on the factors that contribute to the media consumption and punitive relationship, researchers from various disciplines suggest that media can increase public support for harsher punishment strategies. For example, some scholars suggest that the media’s framing effect—which corresponds to how the presentation of issues is characterized (e.g., making them more salient)—can influence consumers’ attitudes and beliefs about social problems (Beale, 2006; Scheufele & Tewksbury, 2006). Others, however, recognize that news coverage of crime-related issues is disproportionately focused on violent and serious criminal events as opposed to nonviolent or nonserious criminal reports (Lipschultz & Hilt, 2014). The plethora of crime and violence-related phenomena presented in the media, in turn, may increase support for punitive policies because consumers’ fear of crime is heightened (Sotirovic, 2001; Surette, 2007).

To date, a growing body of research illustrates that various types of media consumption, such as television news and crime programming, are positively associated with punitive attitudes (Gilliam & Iyengar, 2000; Goidel et al., 2006; Kort-Butler & Hartshorn, 2011; Roche et al., 2016; Rosenberger & Callanan, 2011; Spiranovic et al., 2012; for an exception, see Dowler, 2003). However, it is unclear which media sources are most likely to be associated with punitiveness due to the various ways scholars have examined and/or operationalized both media consumption (e.g., frequency of media consumption in hours/days, types of news and crime-related program examined, and primary source for crime content) and attitudes related to punishment (e.g., Punitive Attitude Scale, support for the death penalty, effectiveness of rehabilitation, and perceived goals of sentencing; see Dowler, 2003; Gilliam & Iyengar, 2000; Roche et al., 2016; Rosenberger & Callanan, 2011; Spiranovic et al., 2012).

Despite the heterogeneous methods by which previous efforts have measured the media consumption and punitive attitudes relationship, several studies have found that consuming television news and/or television crime programming is associated with a variety of punitive outcomes. For example, regarding attitudes about capital punishment, previous efforts have found that consumption of television crime dramas as well as consumption of local television news increase support for the death penalty (Kort-Butler & Hartshorn, 2011; Roche et al., 2016). With respect to attitudes directed toward punitive crime-control policies, Goidel et al. (2006) found that consumption of television news is associated with individuals believing that rehabilitation is not as effective as imprisonment in preventing crime (see also Gilliam & Iyengar, 2000). In relation to general punitive attitudes, Roche et al. (2016) found that local television news and television crime programming consumption is related to increased punitiveness, and Spiranovic et al. (2012) found that those who are less critical of the media and those who consume more television news are positively associated with punitive attitudes. Using a different approach, Rosenberger and Callanan (2011) examined whether media consumption increases the odds that individuals support criminal sentencing goals such as punishment, incapacitation, deterrence, and rehabilitation. Utilizing a sample of more than 4,000 California residents, and rehabilitation as the reference category, the authors found that individuals who consume television news and television crime reality programming were more likely to support punishment over rehabilitation. Furthermore, consumers of television crime dramas were more likely to select incapacitation (as opposed to rehabilitation) as the most important goal of criminal sentencing.

As noted earlier, arguments embedded in audience reception theory state that media consumption may have dissimilar effects on individuals based on their social backgrounds and characteristics. Despite the growing body of work examining media consumption and punitive attitudes, only one known study has investigated whether this relationship varies by audience traits. Using multiple samples within their study, Roche et al. (2016) examined whether Internet news exposure was related to both punitive attitudes and support for the death penalty. The authors found little evidence that consumption of Internet news was related to punitive attitudes across two different samples and their corresponding disaggregated analyses. However, among another sample in their assessment, the authors found that consuming Internet news was negatively associated with supporting the death penalty among various audience characteristics such as gender, race/ethnicity, age, education, income, religiosity, political ideology, and residential area. In fact, across the 18 subsamples examined, 16 negative and significant relationships emerged illustrating robust support for audience reception theory.

Current Study

Previous efforts suggest that media consumption is associated with attitudes related to punishment. Collectively, support for this relationship varies on the types of media consumption considered and the punitive outcome examined. Yet, a significant void in public opinion research on punitiveness is the influence of social media consumption. As a result, there are several unanswered questions regarding how social media consumption may influence penal attitudes. Furthermore, due to only one known study to assess the effects of audience characteristics on the media consumption and punitiveness relationship, the exact pattern of such audience effects is less understood and warrants further investigation. As a result, the purpose of this study is to build on the contributions of previous research by examining whether—and how—various types of social media consumption are related to punitiveness. Similar to the arguments embedded in cultivation theory as well as prior efforts (Gerbner et al., 1980; Morgan et al., 2014; Rosenberger & Callanan, 2011), the following is hypothesized:

Hypothesis 1: Social media consumption will be significantly associated with punitive attitudes.

Second, based on the rationale proposed in audience reception theory (Morgan et al., 2014; Roche et al., 2016), the following is hypothesized:

Hypothesis 2: The relationship(s) between social media consumption and punitiveness will vary based on audience and background characteristics.

Owing to the limited research in this domain, it is unknown which audience characteristics will be the most influential. Yet, the current analysis does disaggregate users by key correlates that have shown to be related to punitiveness.

Data and Method

Sample

Multisite data for this research were conducted and collected through a survey administered to adults attending three universities in the later part of 2016 and the beginning of 2017. The instrument was disseminated to students in accordance with each university’s institutional review board (IRB). Researchers from the three institutions attended various classes, addressing students with an oral description and instructions for the survey. Participants were informed of the voluntary nature of their involvement and anonymity of their responses. Researchers also explained that there were no penalties for nonparticipation and neither extra credit nor incentives were given to those who filled out the survey. Identical information was provided in writing along with contact information for the lead researcher and the corresponding IRB if they had any questions or concerns regarding the study’s protocol. Overall, 34 classes were surveyed among the three campuses.

To gauge attitudes and responses from various social backgrounds, adults were surveyed from not only different regions of the country but also diverse geographical landscapes (e.g., urban, suburban). The majority (51.6%) of the observations were respondents from a large Midwestern university located in a more traditional, semirural “college town.” At this site, 474 students (96.5% response rate) were sampled from 12 criminal justice and criminology courses. The second site, representing the Northeast region (26.8% of the sample) of the United States, is a large urban institution located in a densely populated metropolitan area. Furthermore, this site has a large racial/ethnic minority student population. Here, 246 individuals (95.5% response rate) were sampled from a total of nine social science classes. Finally, 198 adults (94.4% response rate) were surveyed from 13 criminal justice classes at a medium-sized Southern university (21.6%) located in a midsized city. This university has a large “nontraditional” student population (e.g., older adults).

The survey collection across the three sites yielded 918 adults with a total participation rate of 96.1% (918 surveys completed out of 955 collected). The majority of respondents reported being neither criminal justice nor criminology majors (52.8%).³ The final sample demographics consisted of 43.1% male, 54.4% White (18.8% African American, 16.1% Hispanic, 8.7% Other race/ethnicity), and a mean age of 21.1 years.

Measures

Dependent variable

Punitive attitudes were measured using 15 items designed to gauge attitudes toward sanctioning and punishment of offenders. This scale has been identified and used in previous studies (Falco & Martin, 2012; Mackey & Courtright, 2000) and similar items have been used in media-related studies that examined punitiveness (Dowler, 2003; Spiranovic et al., 2012). Respondents were asked to indicate their level of agreement (1 = strongly disagree to 5 = strongly agree) to the following statements: (a) We are entirely too soft on people convicted for crime; (b) offenders should be harshly punished to make them pay for their crimes; (c) we should use the old saying “an eye for an eye and a tooth for a tooth” as a guidance for determining punishment for criminals; (d) to better control the crime problem, more prisons need to be built; (e) prisons today are much too lenient; (f) using the death penalty helps us to better control crime; (g) prison and jail inmates deserve the humiliation, intimidation, and degradation they may receive; (h) drug dealers should be given life sentences for their crime; (i) a person who sexually abuses children should never be released from prison; (j) probation supervision is a joke; (k) a person who has three convictions for very serious crime (felonies) should receive life without the possibility of parole; (l) people choose to commit crimes, therefore, they deserve the punishment they get; (m) harsh and severe punishments are necessary to preserve a sense of justice in our society; (n) speedy, severe, and certain penalties are the only way to prevent people from committing crime; and (o) inmates who participate in programs while confined (such as education, counseling, vocational training) do so only because they are trying to impress the parole board, so they can possibly gain an early release. An exploratory factor analysis (EFA) for the scale resulted in all 15 items loading onto a single factor confirming the scale is unidimensional (eigenvalue = 4.38) with loadings ranging from .32 to .70. Higher scores equate to greater punitive attitudes (α = .84).

It is important to note that due to data being cross sectional, there is no temporal ordering in the key measures examined. Particularly, it is possible that the relationships examined (e.g., various forms of media consumption on punitive attitudes) may be reciprocal. For example, individuals (and characteristics) associated with stronger attitudes directed toward punishing offenders may influence the types of media-related content they choose to consume. The issue of causality is further discussed in the “Discussion” section.

Independent variables

The main independent variables consist of four social media measures that gauge social media consumption in various ways: overall consumption, general news consumption, consumption of news/stories on Facebook involving crime and violence, and consumption of news/stories on Facebook involving the sentencing/punishment of criminals. Consistent with prior research on media consumption (Chiricos, Padgett, & Gertz, 2000; Donovan & Klahm, 2015), overall social media consumption and general news consumption on social media was measured by asking respondents “In a typical week, how much time do you spend . . . ” (a) on social media (such as Facebook, Instagram, Twitter, or Reddit) and (b) reading or watching news stories on social media (such as Facebook, Instagram, Twitter, or Reddit). Response categories included none, 60 min or less, 61 to 120 min, 121 to 180 min, 181 to 240 min, and 241 min or more, and were coded so that higher scores indicated more consumption of the reported media variable.

In addition, due to scholars underlining the importance of examining specific media content related to crime and justice as opposed to general consumption of media (Intravia, Wolff, Paez, & Gibbs, 2017; Roche et al., 2016), measures that gauged the frequency of consuming crime- and violence-related information as well as the sentencing and punishment of offenders on social media were also included. Specific crime/violence consumption and specific sentencing/punishment consumption on social media were examined by asking respondents the following two questions: (a) On Facebook, how often do you read, watch, post, or interact with (such as share, like, or comment) stories or news involving crime or violence occurring in society? (b) On Facebook, how often do you read, watch, post, or interact with (such as share, like, or comment) stories or news involving the sentencing or punishment of criminals? Response categories ranged from 1 = never to 5 = very often.

Control variables

The present study also controlled for several additional media consumption measures, demographics, and correlates of punitiveness. First, five media-related measures were controlled to gauge overall television consumption, overall Internet consumption, television news consumption (local and national), and consumption of television crime shows. Similar to the media variables mentioned above, respondents were asked the following: “In a typical week, how much time do you spend” engaging in the following types of media: (a) watching television (overall consumption), (b) using the Internet, (c) watching a local television news broadcast (such as the 5 p.m. or 10 p.m. news), (d) watching a national television news broadcast (such as CNN or Fox News), and (e) watching television crime shows (such as Criminal Minds, CSI, NCIS, or Law & Order Special Victims Unit). Response categories included none, 60 min or less, 61 to 120 min, 121 to 180 min, 181 to 240 min, and 241 min or more, and were coded so that higher scores indicated greater levels of consumption of the recorded media measure.

Demographics included race (1 = White), sex (1 = male), age (measure continuously), political ideology (1 = very liberal to 5 = very conservative), and residential area (1 = South). Fear of crime was measured by asking respondents to indicate their level of fear (from 0 = not fearful at all to 10 = very fearful) for the following six crime-related events: (a) someone breaking into your home, (b) being robbed or mugged on the street, (c) being sexually assaulted or raped, (d) having your car or bicycle stolen, (e) being beaten up or assaulted by strangers, and (f) being murdered. A factor analysis illustrated that all six items loaded onto a single factor (eigenvalue = 3.92) with loadings ranging from .57 to .90. The six items were summed to obtain a total score. Higher scores equate to greater levels of fear (α = .91). Victimization was a dichotomous measure that asked respondents whether they have been a victim of a crime in the past year (1 = yes). Perceptions of neighborhood problems consisting of six items that asked respondents to indicate “how much of a problem” were the following conditions in their “neighborhood”: (a) vandalism, (b) drunks and drug users, (c) abandoned buildings, (d) burglaries and thefts, (e) run down and poorly kept buildings, and (f) assaults and muggings. Response categories ranged from 1 = a big problem to 3 = not a problem and items were reversed coded and summed to illustrate that higher scores equate to more serious neighborhood problems (α = .85). Finally, a measure of deviant beliefs was controlled. Respondents were asked the following question: “How wrong do you think it is to break the law?” Responses ranged from 1 = not wrong at all to 5 = very wrong.

Descriptive statistics for all variables included in the current study are displayed in Table 1.

Table 1.

Descriptive Statistics for Key Study Variables.

Variables	M (SD)	Range
Punitiveness	43.751 (8.617)	16-83
Race (1 = White)	0.564 (.496)	0-1
Sex (1 = male)	0.431 (.495)	0-1
Age	21.103 (4.837)	18-68
Political status	2.915 (.908)	1-5
Residential area (1 = South)	0.215 (.411)	0-1
Fear of crime	32.218 (17.576)	0-60
Neighborhood problems	8.444 (2.736)	6-18
Deviant beliefs	3.863 (.897)	1-5
Victim (1 = yes)	0.181 (.385)	0-1
Media measures
Television (overall)	2.581 (1.543)	0-5
Internet (overall)	4.196 (1.059)	0-5
Local TV news	0.835 (1.030)	0-5
National TV news	0.854 (1.016)	0-5
Crime TV shows	1.531 (1.537)	0-5
Social media measures
Social media (overall)	3.275 (1.545)	0-5
Social media news (general)	1.935 (1.431)	0-5
Facebook crime (specific)	2.734 (1.239)	1-5
Facebook punish (specific)	2.426 (1.166)	1-5

Analytic Strategy

To examine whether social media consumption is related to punitiveness, a series of ordinary least squares (OLS) regression models were ran using STATA (version 14). Prior to the multivariate analysis, the normality of the dependent variable was examined. A preliminary examination of the dependent variable revealed that its distribution approached normality as the skew and kurtosis were within normal range (skew < 3.0, kurtosis < 10.0). In addition, the normality of the residuals confirms that OLS is an appropriate analytic strategy and the results are likely to be unbiased and reliable. Specifically, White’s (1980) test statistic was assessed to evaluate the null hypothesis that the residuals obtained are homoscedastic. Results of these diagnostic tests indicate that the disturbance terms produced in each model are homoscedastic. Furthermore, variance inflation factors (VIFs) were examined to assure that collinearity was not an issue in the current analysis, and the highest VIF observed was 2.65.

Prior to the estimation of the full models, a series of likelihood-ratio tests were examined to assess whether the key media predictors should be treated as ordinal as opposed to continuous in nature. The likelihood-ratio test compares model fit between the simple model (treating the variable as continuous) and the more complex model (treating the variable as ordinal). If the ordinal results simulate a linear trend, the test statistic will indicate that the simpler continuous model is suitable for the analyses. Controlling for all key covariates, the relationship between each focal media variable and the dependent variable was assessed in a model to determine which fit the data better (not shown in tabular form).⁴ Across all models observed, the results of the diagnostic tests suggested including the measures as continuous, rather than ordinal, fit the data well and did not result in a loss of information or poorer model fit.

Results

The bivariate relationships between each of the variables included in the current analysis are displayed in Table 2. Four of the nine media-related measures are positively associated with punitiveness. These variables include consumption of the following types of media: national television news (.066, p < .05), crime television shows (.079, p < .05), overall social media (.074, p < .05), and specific sentencing/punishment content on Facebook (.079, p < .05). In addition, many of the control variables are also significantly correlated with punitiveness, including race (White; .131, p < .05), age (–.106, p < .01), political status (.305, p < .01), fear of crime (.073, p < .05), neighborhood problems (–.066, p < .05), and deviant beliefs (.160, p < .01).

Table 2.

Bivariate Correlations for Key Study Variables (N = 908).

Variables	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19
1. Punitiveness	1.000
2. Race (1 = White)	.131*	1.000
3. Sex (1 = male)	.016	.034	1.000
4. Age	−.106*	−.148*	.010	1.000
5. Political status	.305*	.249*	.183*	.031	1.000
6. Residential area (1 = South)	−.034	−.100*	−.050	.367*	.001	1.000
7. Fear of crime	.073*	−.225*	−.387*	−.105*	−.118*	0.015	1.000
8. Neighborhood problems	−.066*	−.164*	−.092*	−.023	−.020	0.014	.139*	1.000
9. Deviant beliefs	.160*	.011	−.112*	.055	.107*	.069*	.126*	−.015	1.000
10. Victim (1 = yes)	−.017	.075*	.021	.008	−.034	0.003	−.066*	.061	−.085*	1.000
11. Television (overall)	.049	.120*	−.029	.110*	−.006	.105*	−.052	−.043	−.002	−.012	1.000
12. Internet (overall)	−.022	−.022	.005	−.077*	−.074*	−0.046	.038	.030	−.056	−.061	.098*	1.000
13. Local TV news	.015	−.098*	−.049	.207*	−.019	.101*	.027	.031	.025	−.003	.219*	.014	1.000
14. National TV news	.066*	−.012	.002	.189*	.021	0.059	−.047	.032	.062	.040	.206*	−.005	.607*	1.000
15. Crime TV shows	.079*	−.038	−.284*	.019	−.050	0.007	.116*	.045	.018	.031	.305*	.051	.201*	.189*	1.000
16. Social media (overall)	.074*	−.042	−.214*	−.172*	−.136*	−0.037	.196*	−.014	−.001	−.040	.181*	.394*	.080*	.052	.182*	1.000
17. Social media news (general)	.032	−.066*	−.123*	−.078*	−.109*	−0.024	.110*	.043	−.071	.061	.104*	.252*	.221*	.205*	.254*	.491*	1.000
18. Facebook crime (specific)	.035	−.044	−.146*	−.025	−.079*	0.056	.134*	.052	.032	−.007	.029	.103*	.107*	.107*	.134*	.278*	.342*	1.000
19. Facebook punish (specific)	.079*	−.071*	−.119*	.005	−.066*	.083*	.121*	.034	.013	.010	.020	.065*	.144*	.131*	.143*	.249*	.327*	.270*	1.000

p < .05.

Table 3 presents the results of the multivariate analyses. Model 1 of Table 3 is the baseline model, which includes demographics and key correlates of punitiveness. Results illustrate that punitive attitudes tend to be harsher among individuals who are younger, more conservative, have higher levels of fear, perceive fewer neighborhood problems, and believe that breaking the law is wrong (deviant beliefs). A comparison of the standardized coefficients illustrate that political status (β = .289), fear of crime (β = .108), and deviant beliefs (β = .120) are the strongest correlates in the model. In Model 2 of Table 3, all media consumption controls are included. The only media-related variable that was significant was consumption of crime-related television shows, which suggests that individuals who consume more crime-related content on television are associated with greater levels of punitiveness. In Models 3 through 6, the various types of social media consumption are introduced individually (overall, general news, crime specific, and punishment specific). Model 3 of Table 3 shows that individuals who consume more social media overall are more likely to be punitive. In Model 4, general consumption of news on social media is not significantly related to penal attitudes. Similar results are present in Model 5, which illustrates that consumption of crime- and violence-related content on Facebook was not significantly related to punitiveness. In Model 6, consumption of news and information on Facebook about punishment was significantly related to punitive attitudes. This suggests that consuming specific content about sentencing and punishment of offenders on social media is associated with punitiveness. It is important to note that all the controls that were significant in Model 1, which included age, political status, fear of crime, perceived neighborhood problems, and deviant beliefs, remained significant across Models 2 through 6. Furthermore, when comparing the standardized coefficients for the four different social media measures in Models 2 through 6, overall social media consumption (β = .086) had the strongest effect on punitiveness followed by specific punishment consumption (β = .081). Finally, Model 7 of Table 3 presents the full model of results that include all demographics, correlates, and media-related consumption measures. As shown in Model 7, among all the media-related measures considered, consuming sentencing/punishment content on Facebook had the strongest media effect on attitudes related to punishment (β = .121) and is the only media consumption measure that is significantly related to punitiveness. Specifically, individuals who reported consuming more news and information about the sentencing and punishment of offenders on social media are associated with more punitive attitudes. Once again, age, political status, fear of crime, neighborhood problems, and deviant beliefs remained significantly related to punitiveness.

Table 3.

OLS Regression Models With Key Study Variables (N = 908).

Variables	Model 1			Model 2			Model 3			Model 4			Model 5			Model 6			Model 7
Variables	b	SE	β	b	SE	β	b	SE	β	b	SE	β	b	SE	β	b	SE	β	b	SE	β
Race (1 = White)	0.979	0.588	.056	0.780	0.597	.050	0.926	0.596	.053	0.893	0.598	.051	0.868	0.597	.049	0.905	0.596	.052	0.971	0.596	.055
Sex (1 = male)	0.242	0.597	.013	0.610	0.619	.035	0.830	0.624	.047	0.640	0.619	.036	0.661	0.620	.037	0.681	0.618	.039	0.816	0.624	.046
Age	−0.186**	0.061	−.104	−0.216**	0.061	−.121	−0.189**	0.062	−.106	−0.209**	0.062	−.117	−0.212**	0.061	−.119	−0.209**	0.061	−.117	−0.190**	0.062	−.107
Political status	2.743**	0.312	.289	2.763**	0.312	.290	2.807**	0.312	.295	2.780**	0.312	.292	2.779**	0.312	.292	2.787**	0.311	.293	2.805**	0.311	.295
Residential area (1 = South)	−0.001	0.700	−.001	0.031	0.703	.001	0.012	0.701	.001	0.028	0.703	.001	−0.023	0.705	−.001	−0.121	0.704	−.005	−0.113	0.703	−.005
Fear of crime	0.053**	0.017	.108	0.054**	0.017	.111	0.050**	0.017	.103	0.054**	0.017	.109	0.053**	0.017	.109	0.051**	0.017	.105	0.049**	0.017	.100
Neighborhood problems	−0.204**	0.099	−.065	−0.217*	0.099	−.069	−0.200*	0.099	−.063	−0.216*	0.099	−.068	−0.219*	0.099	−.069	−0.216*	0.099	−.068	−0.196*	0.099	−.062
Deviant beliefs	1.152**	0.305	.120	1.134**	0.305	.117	1.134**	0.304	.117	1.159**	0.306	.120	1.129**	0.305	.117	1.139**	0.304	.118	1.146**	0.305	.119
Victim (1 = yes)	0.221	0.698	.009	0.104	0.699	.004	0.110	0.697	.004	0.049	0.701	.002	0.107	0.699	.004	0.079	0.697	.003	0.078	0.698	.003
Media measures
Television (overall)	—	—	—	0.162	0.189	.029	0.098	0.190	.017	0.164	0.189	.029	0.168	0.189	.030	0.184	0.188	.033	0.126	0.190	.022
Internet (overall)	—	—	—	−0.089	0.255	−.010	−0.341	0.275	−.041	−0.159	0.263	−.019	−0.119	0.256	−.014	−0.129	0.255	−.015	−0.309	0.276	−.037
Local TV news	—	—	—	−0.155	0.336	−.018	−0.180	0.336	−.021	−0.194	0.338	−.022	−0.168	0.336	−.019	−0.207	0.336	−.024	−0.224	0.337	−.026
National TV news	—	—	—	0.631	0.336	.074	0.606	0.335	.071	0.592	0.337	.069	0.605	0.336	.071	0.573	0.335	.067	0.577	0.336	.067
Crime TV shows	—	—	—	0.432*	0.193	.076	0.404*	0.193	.071	0.395*	0.196	.070	0.415*	0.193	.073	0.387*	0.193	.068	0.377	0.195	.067
Social media measures
Social media (overall)	—	—	—	—	—	—	0.481*	0.201	.086	—	—	—				—	—	—	0.431	0.221	.077
Social media news (general)	—	—	—	—	—	—	—	—	—	0.223	0.206	.037	—	—	—	—	—	—	−0.040	0.230	−.006
Facebook crime (specific)	—	—	—	—	—	—	—	—	—	—	—	—	0.262	0.222	.038	—	—	—	−0.475	0.347	−.068
Facebook punish (specific)	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	0.601*	0.234	.081	0.896*	0.366	.121
Constant	34.543**	2.198		34.034**	2.531		32.953**	2.564		33.727**	2.546		33.431**	2.581		32.692**	2.577		32.212**	2.600
R ²	.138			.152			.158			.154			.154			.159			.164

Note. OLS = ordinary least squares.

p < .05. **p < .01.

Overall, and consistent with the arguments grounded in the cultivation framework, consumption of specific content related to sentencing and punishment on social media is significantly related to punitive attitudes. All other media-related measures considered were not significant, suggesting that consuming detailed material about sentencing/punishment, as opposed to overall consumption or general news consumption, is more strongly associated with attitudes related to punishment.

Disaggregating Media Audiences

As noted above, audience reception theory contends that media consumption varies based on users’ demographics and social backgrounds. Stated differently, the characteristics of individuals may influence the reception of media-related messages consumed from media. Similar to previous efforts on media consumption testing reception research (Chiricos et al., 2000; Weitzer & Kubrin, 2004), only media measures that were significantly related to the outcome in the full model (Model 7 of Table 2) are further assessed. Specifically, the additional analysis examines whether the relationship between specific consumption of punishment content on social media and punitiveness is conditional on select respondent characteristics.

Table 4 shows the results for the disaggregated analysis on key individual characteristics and correlates of punitiveness such as race, sex, political affiliation, residential location, victimization, and fear of crime.⁵ The results from the subsample analysis illustrates that among Whites, males, those living outside of the South, nonvictims, and those with lower levels of fear, consuming specific content about sentencing/punishment on social media is associated with greater levels of punitiveness. There were no significant effects of media consumption among non-Whites, females, political affiliation (conservative or liberal/moderate), crime victims, those living in the South, and those with higher levels of fear.

Table 4.

Demographic Subsamples Analysis for Social Media Punishment Consumption.

Variables	N	Facebook punish
Variables	N	b (SE)
White	513	1.411 (0.473)**
Non-White	395	0.320 (0.588)
Test for equality of coefficients	z	1.367
Male	390	1.230 (0.596)*
Female	518	0.826 (0.426)
Test for equality of coefficients	z	0.563
Conservative	212	1.188 (0.790)
Liberal or moderate	696	0.426 (0.271)
Test for equality of coefficients	z	0.152
South	194	0.454 (0.848)
Non-South	714	1.057 (0.409)*
Test for equality of coefficients	z	0.758
Victim	166	0.160 (0.909)
Nonvictim	742	0.863 (0.401)*
Test for equality of coefficients	z	0.570
High fear	454	0.138 (0.543)
Low fear	454	1.547 (0.509)**
Test for equality of coefficients	z	1.818*

Note. Models include all reported control variables presented in Table 3.

p < .05. **p < .01.

To test the significance of the differences between the groups specified in Table 4, following the lead of Paternoster, Brame, Mazerolle, and Piquero (1998), a z test for equality of regression coefficients (i.e., slope difference test) was conducted. This test determines whether the null hypothesis between two regression coefficients (e.g., male and female) are equal. Results of these tests suggest that the effect between consumption of specific punishment content on social media and punitiveness varies significantly between low and high levels of fear (z = 1.818, p < .05). There were no significant differences observed among the other subgroups examined.⁶

Discussion

Ever since Gerbner and colleagues (Gerbner et al., 1977; Gerbner et al., 1980) suggested that mainstream television can cultivate consumers into thinking that the real world is a scary place, researchers from various disciplines have been fascinated with examining the effect of media consumption on an array of crime- and justice-related outcomes. The purpose of this research was to expand on previous media consumption and punitiveness efforts in two important ways: (a) Examine the effects of various types of social media consumption on attitudes related to punishment, controlling for traditional forms of media consumption and key correlates; and (b) disaggregate the audience by select characteristics related to punishment to determine whether the media consumption and punitiveness relationship differs across specific factors or demographics. To date, this is the only known study to consider social media consumption on penal attitudes and the second known study to disaggregate the media consumption and punitiveness relationship by audience characteristics. The inclusion of social media consumption measures is salient, given that recent reports illustrate that two thirds (67%) of U.S. adults receive news from social media platforms (Shearer & Gottfried, 2017). Furthermore, individuals aged 18 to 49 years are more likely to receive news and information from online sources (e.g., social media, websites) compared with television, radio, and printed newspapers (Mitchell, Gottfried, Barthel, & Shearer, 2016), and the overall gap between the total percentage of U.S. adults who often get their news between television and online is drastically narrowing (Bialik & Matsa, 2017).

Using a multisite sample of mostly young adults, the results revealed that consuming specific content on social media about sentencing/punishment is associated with stronger attitudes directed toward punishment. This is not surprising, given that scholars have contended that more nuanced consumption measures (as opposed to overall or general consumption measures) may be important “in the social construction of popular ideas about key social issues such as crime and punishment” (Roche et al., 2016, p. 233). Furthermore, although the cross-sectional nature of this study cannot directly provide support for cultivation theory, the results are consistent with the underlying arguments found in the cultivation framework. Specifically, messages depicted in the media may shape or influence individuals’ conception of reality (Gerbner, 1969). In addition, other lines of work investigating media consumption and attitudes directed toward the police have found that content-specific media measures such as consuming crime- or policing-related television shows affect individuals’ perceptions of the police (Callanan & Rosenberger, 2011; Donovan & Klahm, 2015; Dowler & Zawilski, 2007).

When turning to the disaggregated analysis, the results showed that the relationship between consuming punishment-related content on social media and punitive attitudes varied by race, sex, residential location, victimization, and fear of crime. However, a closer examination between the subgroup differences illustrated that the differences between the subsample slopes were only significantly different between levels of fear. That is, the consumption of sentencing/punishment content on social media is positively associated with punitiveness among individuals with low levels of fear. This finding is consistent with the substitution perspective, which contends that media effects may be more pronounced among consumers without personal experiences with crime and the criminal justice system (Gunter, 1987; Liska & Baccaglini, 1990). Thus, individuals who have lower emotional responses to fear may be more susceptible to the messages depicted in punishment-specific content on social media, which is ultimately associated with harsher attitudes directed toward punishment.

This study is not without limitations. First, the sample was limited to mostly young adults (approximately 75% of the sample were 21 years of age or younger). As a result of the restricted range in the distribution of participants’ ages, the current study was unable to examine whether the social media and punitive relationship varied by the age of respondents. In addition, the current study relied on a nonrandom sample of college students. Previous research illustrates that college students, and specifically criminal justice/criminology majors, tend to be more punitive (see Falco & Martin, 2012; Mackey & Courtright, 2000). Thus, the nature of the sample may have potentially influenced the study’s findings and the results cannot be generalized to the public. It is recommended that future studies replicate this research using a different sample of adults who are more diverse across age and more generalizable in nature. Second, the current study did not have information on religiosity, income, racial typification of crime, or perceived/actual levels of crime. Previous efforts have shown that these measures are associated with punitiveness (Applegate et al., 2000; Dowler, 2003; Roberts & Indermaur, 2007; Spiranovic et al., 2012). Controlling for these known punitive correlates may detect different results and they may point to additional subsample findings. Third, although the study measured two types of specific media consumption on Facebook, the response of these measures can be interpreted as subjective (e.g., responses ranged from never to very often). Thus, it is recommended that future research measure the actual amount of time spent consuming specific social media content in a given time period (e.g., less than 60 min, more than 60 min) as opposed to how often one reports consuming specific media content (e.g., never to very often).

Fourth, this study, as well as many previous assessments on media consumption and criminal justice outcomes, are limited to cross-sectional data. As a result, the empirical direction of the relationships needs to be interpreted with caution, and it is recommended that future research utilize a longitudinal design to address the issues of temporal ordering in the media consumption and punitiveness relationship. For example, it would be interesting to gauge whether high-profiled events involving the punishment of criminals, police, and public figures affect punitiveness among media consumers. Furthermore, it would be fascinating to examine how the relationship between social media consumption and penal attitudes vary before and after political elections. Finally, the current study did not collect information pertaining to social media users’ habits and motivations. That is, do some users prefer one social media platform over another to obtain news and information related to crime and justice? Also, are social media users more likely to show engagement on crime-related issues (e.g., sharing stories, commenting) on certain social media sites over others? And, if so, do users trust the information they receive from social media and their individual networks that may share content? In sum, there are many promising avenues for future research between media cultivation and attitudes and perceptions related to crime and justice.

The findings from this study have policy implications with respect to public opinion on issues related to punishment. As shown in the current study, consuming specific content related to the sentencing and punishment of offenders is associated with harsher punitive attitudes. Because media coverage on crime- and justice-related issues may influence policy decisions (Beale, 2006; Kort-Butler & Hartshorn, 2011), it is important for policy makers to take into consideration the nature and accuracy of stories/news covered in the media to inform and educate consumers about accurate punishments and policies associated with law violation (Spiranovic et al., 2012). In addition, prior research illustrates that media reliance is associated with reduced knowledge about criminal justice policies (Pickett et al., 2015). Thus, it is important for policy makers to make decisions based on evidence of punishment and/or treatment programs as opposed to public sentiments formed from consuming mass media (Thompson, 2010). Finally, because the number of individuals who turn to social media for news and information has steadily increased in the past 10 years, it may be beneficial to use social media platforms to educate consumers about the criminal justice system (Pickett et al., 2015).

In closing, despite the data limitations, the current study provides empirical support for cultivation theory with a widely unknown media influence, such a social media. As a result, the findings in this study not only add to the growing research in the cultivation framework but also provide an important advancement for understanding whether and how contemporary forms of media consumption influence consumers’ attitudes related to crime and the criminal justice system.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

References

Anderson

A. A.

Brossard

Scheufele

D. A.

Xenos

M. A.

Ladwig

(2014). The “nasty effect”: Online incivility and risk perceptions of emerging technologies. Journal of Computer-Mediated Communication, 19, 373-387.

Anderson

(2016). Social media causes some users to rethink their views on an issue. Retrieved from http://www.pewresearch.org/fact-tank/2016/11/07/social-media-causes-some-users-to-rethink-their-views-on-an-issue/

Applegate

B. K.

Cullen

F. T.

Fisher

B. S.

(2002). Public views toward crime and correctional policies: Is there a gender gap? Journal of Criminal Justice, 30, 89-100.

Applegate

B. K.

Cullen

F. T.

Fisher

B. S.

Vander Ven

(2000). Forgiveness and fundamentalism: Reconsidering the relationship between correctional attitudes and religion. Criminology, 38, 719-754.

Armborst

(2017). How fear of crime affects punitive attitudes. European Journal on Criminal Policy and Research, 23, 461-481.

Bader

C. D.

Desmond

S. A.

Mencken

F. C.

Johnson

B. R.

(2010). Divine justice: The relationship between images of God and attitudes toward criminal punishment. Criminal Justice Review, 35, 90-106.

Barkan

S. E.

Cohn

S. F.

(2005). Why whites favor spending more money to fight crime: The role of racial prejudice. Social Problems, 52, 300-314.

Beale

S. S.

(2006). The news media’s influence on criminal justice policy: How market-driven news promotes punitiveness. William & Mary Law Review, 48, 397-481.

Bialik

Matsa

K. E.

(2017, October 4). Key trends in social and digital news media. Retrieved from http://www.pewresearch.org/fact-tank/2017/10/04/key-trends-in-social-and-digital-news-media/

10.

Blalock

H. M.

(1967). Toward a theory of minority-group relations. Hoboken, NJ: John Wiley.

11.

Callanan

V. J.

Rosenberger

J. S.

(2011). Media and public perceptions of the police: Examining the impact of race and personal experience. Policing and Society, 21, 167-189.

12.

Chiricos

Eschholz

(2002). The racial and ethnic typification of crime and the criminal typification of race and ethnicity in local television news. Journal of Research in Crime and Delinquency, 39, 400-420.

13.

Chiricos

Padgett

Gertz

(2000). Fear, TV news, and the reality of crime. Criminology, 38, 755-786.

14.

Chiricos

Welch

Gertz

(2004). Racial typification of crime and support for punitive measures. Criminology, 42, 358-390.

15.

Cochran

J. K.

Chamlin

M. B.

(2006). The enduring racial divide in death penalty support. Journal of Criminal Justice, 34, 85-99.

16.

Costelloe

M. T.

Chiricos

Gertz

(2009). Punitive attitudes toward criminals: Exploring the relevance of crime salience and economic insecurity. Punishment & Society, 11, 25-49.

17.

Costelloe

M. T.

Chiricos

Buriánek

Gertz

Maier-Katkin

. (2002). The social correlates of punitiveness toward criminals: A comparison of the Czech Republic and Florida. Justice System Journal, 23, 191-220.

18.

Cullen

F. T.

Clark

G. A.

Cullen

J. B.

Mathers

R. A.

(1985). Attribution, salience, and attitudes toward criminal sanctioning. Criminal Justice and Behavior, 12, 305-331.

19.

Cullen

F. T.

Fisher

B. S.

Applegate

B. K.

(2000). Public opinion about punishment and corrections. Crime and Justice, 27, 1-79.

20.

Cummings

Gottshall

(2014). Citizenship and consumption. In Fortner

R. S.

Fackler

P. M.

(Eds.), The handbook of media and mass communication theory (pp. 612-628). Malden, MS: John Wiley & Sons.

21.

Dixon

T. L.

Linz

(2000). Overrepresentation and underrepresentation of African Americans and Latinos as lawbreakers on television news. Journal of Communication, 50, 131-154.

22.

Donley

A. M.

Gualtieri

M. C.

(2017). “Homeless killer”: An analysis of the media’s portrayal of the victims of a serial killer. Deviant Behavior, 38, 226-239.

23.

Donovan

K. M.

Klahm

C. F.

IV . (2015). The role of entertainment media in perceptions of police use of force. Criminal Justice and Behavior, 42, 1261-1281.

24.

Doob

A. N.

Macdonald

G. E.

(1979). Television viewing and fear of victimization: Is the relationship causal? Journal of Personality and Social Psychology, 37, 170-179.

25.

Dowler

(2003). Media consumption and public attitudes toward crime and justice: The relationship between fear of crime, punitive attitudes, and perceived police effectiveness. Journal of Criminal Justice and Popular Culture, 10, 109-126.

26.

Dowler

Zawilski

(2007). Public perceptions of police misconduct and discrimination: Examining the impact of media consumption. Journal of Criminal Justice, 35, 193-203.

27.

Enns

P. K.

(2014). The public’s increasing punitiveness and its influence on mass incarceration in the United States. American Journal of Political Science, 58, 857-872.

28.

Evans

T. D.

Adams

(2003). Salvation or damnation? Religion and correctional ideology. American Journal of Criminal Justice, 28, 15-35.

29.

Falco

D. L.

Martin

J. S.

(2012). Examining punitiveness: Assessing views toward the punishment of offenders among criminology and non-criminology students. Journal of Criminal Justice Education, 23, 205-232.

30.

Franklin

C. A.

Franklin

T. W.

(2009). Predicting fear of crime: Considering differences across gender. Feminist Criminology, 4, 83-106.

31.

Frost

N. A.

(2010). Beyond public opinion polls: Punitive public sentiment & criminal justice policy. Sociology Compass, 4, 156-168.

32.

Garland

Wodahl

Schuhmann

(2013). Value conflict and public opinion toward prisoner reentry initiatives. Criminal Justice Policy Review, 24, 27-48.

33.

Garofalo

(1981). The fear of crime: Causes and consequences. The Journal of Criminal Law and Criminology, 72, 839-857.

34.

Gerbner

(1969). Toward “cultural indicators”: The analysis of mass mediated public message systems. Educational Technology Research & Development, 17(2), 137-148.

35.

Gerbner

Gross

. (1976). Living with television: The violence profile. Journal of communication, 26, 172-199.

36.

Gerbner

Gross

Eleey

M. F.

Jackson-Beeck

Jeffries-Fox

Signorielli

(1977). TV violence profile no. 8: The highlights. Journal of Communication, 27, 171-180.

37.

Gerbner

Gross

Morgan

Signorielli

(1980). The “mainstreaming” of America: Violence profile no. 11. Journal of Communication, 30(3), 10-29.

38.

Gilliam

F. D.

Jr. Iyengar

(2000). Prime suspects: The influence of local television news on the viewing public. American Journal of Political Science, 44, 560-573.

39.

Goidel

R. K.

Freeman

C. M.

Procopio

S. T.

(2006). The impact of television viewing on perceptions of juvenile crime. Journal of Broadcasting & Electronic Media, 50, 119-139.

40.

Gross

Aday

(2003). The scary world in your living room and neighborhood: Using local broadcast news, neighborhood crime rates, and personal experience to test agenda setting and cultivation. Journal of Communication, 53, 411-426.

41.

Gunter

(1987). Television and the fear of crime. London, England: John Libbey.

42.

Hirsch

P. M.

(1980). The “scary world” of the nonviewer and other anomalies: A reanalysis of Gerbner et al.’s findings on cultivation analysis. Part I. Communication Research, 7, 403-456.

43.

Hughes

(1980). The fruits of cultivation analysis: A reexamination of some effects of television watching. Public Opinion Quarterly, 44, 287-302.

44.

Intravia

Piquero

A. R.

Piquero

N. L.

(2018). The racial divide surrounding United States of America national anthem protests in the national football league. Deviant Behavior, 39, 1058-1068.

45.

Intravia

Wolff

K. T.

Paez

Gibbs

B. R.

(2017). Investigating the relationship between social media consumption and fear of crime: A partial analysis of mostly young adults. Computers in Human Behavior, 77, 158-168.

46.

Johnson

(2007). Crime salience, perceived racial bias, and Blacks’ punitive attitudes. Journal of Ethnicity in Criminal Justice, 4(4), 1-18.

47.

Johnson

(2009). Anger about crime and support for punitive criminal justice policies. Punishment & Society, 11, 51-66.

48.

Jones

J. M.

(2017, October 26). U.S. death penalty support lowest since 1972. Gallup. Retrieved from http://news.gallup.com/poll/221030/death-penalty-support-lowest-1972.aspx

49.

King

R. D.

Wheelock

(2007). Group threat and social control: Race, perceptions of minorities and the desire to punish. Social Forces, 85, 1255-1280.

50.

Kleck

Jackson

D. B.

(2017). Does crime cause punitiveness? Crime & Delinquency, 63, 1572-1599.

51.

Kort-Butler

L. A.

Hartshorn

K. J. S.

(2011). Watching the detectives: Crime programming, fear of crime, and attitudes about the criminal justice system. The Sociological Quarterly, 52, 36-55.

52.

Kury

Ferdinand

(1999). Public opinion and punitivity. International Journal of Law and Psychiatry, 22, 373-392.

53.

Lipschultz

J. H.

Hilt

M. L.

(2014). Crime and local television news: Dramatic, breaking, and live from the scene. Mahwah, NJ: L. Earlbaum Associates.

54.

Liska

A. E.

(Ed.). (1992). Social threat and social control. Albany: State University of New York Press.

55.

Liska

A. E.

Baccaglini

(1990). Feeling safe by comparison: Crime in the newspapers. Social Problems, 37, 360-374.

56.

Mackey

D. A.

Courtright

K. E.

(2000). Assessing punitiveness among college students: A comparison of criminal justice majors with other majors. Criminal Justice Studies, 12, 423-441.

57.

McCall

J. M.

(2007). Viewer discretion advised: Taking control of mass media influences. Lanham, MD: Rowman & Littlefield.

58.

McCorkle

R. C.

(1993). Research note: Punish and rehabilitate? Public attitudes toward six common crimes. NCCD News, 39, 240-252.

59.

Menifield

C. E.

Rose

W. H.

Homa

Cunningham

A. B.

(2001). The media’s portrayal of urban and rural school violence: A preliminary analysis. Deviant Behavior, 22, 447-464.

60.

Metcalfe

Pickett

J. T.

Mancini

(2015). Using path analysis to explain racialized support for punitive delinquency policies. Journal of Quantitative Criminology, 31, 699-725.

61.

Mitchell

Gottfried

Barthel

Shearer

(2016). The modern news consumer: News attitudes and practices in the digital era (Journalism project). Pew Research Center.

62.

Morgan

Shanahan

. (2010). The state of cultivation. Journal of Broadcasting & Electronic Media, 54, 337-355.

63.

Morgan

Shanahan

Signorielli

(2014). Cultivation theory in the twenty-first century. In Fortner

R. S.

Fackler

P. M.

(Eds.), The handbook of media and mass communication theory (pp. 480-497). Hoboken, NJ: John Wiley.

64.

Nellis

A. M.

Savage

(2012). Does watching the news affect fear of terrorism? The importance of media exposure on terrorism fear. Crime & Delinquency, 58, 748-768.

65.

Paternoster

Brame

Mazerolle

Piquero

(1998). Using the correct statistical test for the equality of regression coefficients. Criminology, 36, 859-866.

66.

Pew Charitable Trusts. (2016, February 12). Voters want big changes in federal sentencing, prison system. Retrieved from http://www.pewtrusts.org/en/research-and-analysis/analysis/2016/02/12/voters-want-changes-in-federal-sentencing-prison-system

67.

Pew Research Center. (2017, January 12). Social media fact sheet. Retrieved from http://www.pewinternet.org/fact-sheet/social-media/

68.

Pickett

J. T.

Chiricos

(2012). Controlling other people’s children: Racialized views of delinquency and whites’ punitive attitudes toward juvenile offenders. Criminology, 50, 673-710.

69.

Pickett

J. T.

Mancini

Mears

D. P.

Gertz

(2015). Public (mis) understanding of crime policy: The effects of criminal justice experience and media reliance. Criminal Justice Policy Review, 26, 500-522.

70.

Piquero

A. R.

Piquero

N. L.

Gertz

Baker

Batton

Barnes

J. C.

(2011). Race, punishment, and the Michael Vick experience. Social Science Quarterly, 92, 535-551.

71.

Ramirez

M. D.

(2013). Americans’ changing views on crime and punishment. Public Opinion Quarterly, 77, 1006-1031.

72.

Roberts

L. D.

Indermaur

(2007). Predicting punitive attitudes in Australia. Psychiatry, Psychology and Law, 14, 56-65.

73.

Rocha

R. R.

Espino

(2009). Racial threat, residential segregation, and the policy attitudes of Anglos. Political Research Quarterly, 62, 415-426.

74.

Roche

S. P.

Pickett

J. T.

Gertz

(2016). The scary world of online news? Internet news exposure and public attitudes toward crime and justice. Journal of Quantitative Criminology, 32, 215-236.

75.

Rosenberger

J. S.

Callanan

V. J.

(2011). The influence of media on penal attitudes. Criminal Justice Review, 36, 435-455.

76.

RTI International. (2017, March 17). Majority of Americans believe role of jails should not be to punish. Retrieved from https://www.rti.org/news/majority-americans-believe-role-jails-should-not-be-punish

77.

Scheufele

D. A.

Tewksbury

(2006). Framing, agenda setting, and priming: The evolution of three media effects models. Journal of Communication, 57, 9-20.

78.

Shearer

Gottfried

(2017). News use across social media platforms 2017. Retrieved from http://www.journalism.org/2017/09/07/news-use-across-social-media-platforms-2017/

79.

Shirley

K. E.

Gelman

(2015). Hierarchical models for estimating state and demographic trends in US death penalty public opinion. Journal of the Royal Statistical Society, Series A: Statistics in Society, 178, 1-28.

80.

Skogan

W. G.

Maxfield

M. G.

(1981). Coping with crime: Individual and neighborhood reactions. Beverly Hills, CA: Sage Publications.

81.

Sotirovic

(2001). Affective and cognitive processes as mediators of media influences on crime-policy preferences. Mass Communication and Society, 4, 311-329.

82.

Spiranovic

C. A.

Roberts

L. D.

Indermaur

(2012). What predicts punitiveness? An examination of predictors of punitive attitudes towards offenders in Australia. Psychiatry, Psychology and Law, 19, 249-261.

83.

Surette

(2007). Media, crime, and criminal justice: Images, realities, and policies. Belmont, CA: Thomas Wadsworth.

84.

Thompson

A. C.

(2010). From sound bites to sound policy: Reclaiming the high ground in criminal justice policy-making. Fordham Urban Law Journal, 38, 775-820.

85.

Tonry

(2004). Thinking about crime: Sense and sensibility in American penal culture. Oxford, UK: Oxford University Press.

86.

Unnever

J. D.

Cullen

F. T.

Applegate

B. K.

(2005). Turning the other cheek: Reassessing the impact of religion on punitive ideology. Justice Quarterly, 22, 304-339.

87.

Van Kesteren

. (2009). Public attitudes and sentencing policies across the world. European Journal on Criminal Policy and Research, 15, 25-46.

88.

Walmsley

(2016). World prison brief. London, England: Institute for Criminal Policy Research. Retrieved from http://www.prisonstudies.org/world-prison-brief

89.

Weitzer

Kubrin

C. E.

(2004). Breaking news: How local TV news and real-world conditions affect fear of crime. Justice Quarterly, 21, 497-520.

90.

Welch

Payne

A. A.

Chiricos

Gertz

(2011). The typification of Hispanics as criminals and support for punitive crime control policies. Social Science Research, 40, 822-840.

91.

White

(1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica: Journal of the Econometric Society, 48, 817-838.

92.

Wozniak

K. H.

(2016). Public opinion and the politics of criminal justice policy making: Reasons for optimism, pessimism, and uncertainty. Criminology & Public Policy, 15, 179-186.