Reading 6.2 We Don’t Like Your Type Around Here: Regional and Residential Differences in Exposure to Online Hate Material Targeting Sexuality

Matthew Costello, Joseph Rukus, and James Hawdon

Introduction

On September 10, 2010, 18-year-old Tyler Clementi committed suicide by jumping off the George Washington Bridge. In a case that received national attention, Clementi, an openly gay student at Rutgers University, was the victim of online bullying and harassment. Clementi became the target of online ridicule when unauthorized footage of him kissing another man was streamed. On January 29, 2012, openly gay 14-year-old Rafael Morelos hanged himself. Like Clementi, Morelos was the victim of bullying—both online and offline— based on his sexual orientation. A phony Facebook page was created by one of Morelos’s harassers in order to taunt and torment him.

Online hate, bullying, and harassment are growing problems that can result in a host of injurious outcomes, including diminished levels of well-being (Keipi et al. 2017) and trust (Nasi et al. 2015), acts of violence (Freilich et al. 2014; Daniels 2008), and suicide (Russell and

Joyner 2001). These negative outcomes are more likely when the hate is aimed at vulnerable populations. Anti-lesbian, gay, bisexual, transgender, questioning (LGBTQ) hate has an especially negative psychological impact on those affected because it attacks a core tenet of the target’s identity and community membership (Garnets, Herek, and Levy 1990; Herek 2009; Herek and Garnets 2007; Herek, Gillis, and Cogan 1999). Therefore, understanding factors that contribute to the genesis of online hate based on sexual orientation is critical.

The ubiquity of the internet has led to a spike in the quantity of hate material online. Although the anonymity, immediacy, and global reach of the internet offer benefits that are evident, these same attributes also create avenues for hatemongers to promote their ideology and grow their ranks. It is precisely the open and egalitarian nature of the internet that renders cyberspace an ideal breeding ground for hate groups. Indeed, the dramatic increase in online hate over the past 20 years serves as stark evidence. Though the nomadic nature of hate sites makes it nearly impossible to measure the number of online groups active at any given time, there is consensus that the number is growing, and at a rapid rate (Banks 2010; Hawdon, Oksanen, and Rasanen 2014; Potok 2015).

Recent research has sought to understand correlates of exposure to, and targeting by, online hate (Costello et al. 2016; Costello, Hawdon, and Ratliff 2016; Hawdon, Oksanen, and Rasanen 2014). Much of this work has focused on online exposure and targeting broadly defined, though. Less attention has been afforded to the examination of particular types of targeting based on attributes such as sexuality, the focus of this paper. Further, this study pays particular heed to one’s geographic place of residence as a potentially salient predictor of victimization. Most existing studies of cybervictimization do not consider the influence of locality; however, residence might be relevant when examining hate related to sexuality, given the opposition to lesbian, gay, bisexual, and transgender (LGBT) rights in certain parts of the country (Smith 1997). For example, public opinion polls consistently show that levels of sexual prejudice are higher amongst people living in the South and rural areas (Bau- nach, Burgess, and Muse 2009), and lesbian, gay, and bisexual individuals have reported higher levels of enacted sexual stigma in rural areas (Swank, Fahs, and Frost 2013).

To examine targeting of online hate based on sexual orientation, we examine the following hypotheses;

Hypothesis #1: Individuals who spend more time online, or engage in antagonistic behaviors online, are more likely to be targeted by online hate based on sexual orientation.

Hypothesis #2: Individuals with more robust support systems, online and offline, are less likely to be targeted by online hate based on sexual orientation.

Hypothesis #3: Individuals living in the Southern region of the United States are more likely to be targeted by online hate based on sexual orientation.

Hypothesis #4; Individuals living in a rural area are more likely to be targeted by online hate based on sexual orientation.

Literature Review

An Overview of Online Hate

Online hate expresses hatred of a group (see Hawdon, Oksanen, and Rasanen 2016). Although it shares similarities with cyberbully- ing and cyberstalking, cyberhate focuses on a collective, not individuals in isolation. Online hate can take many forms, though most commonly it expresses attitudes that demean or degrade others based on their race/ethnicity, national origin, sexual orientation, gender, gender identity, or other characteristics.

Tracking online hate is a formidable task. As hate groups build, dismantle, and move their web presence, tallying an accurate count of groups is nearly impossible. Nevertheless, the best available information suggests that online hate groups proliferated between 2000 and 2011, increasing by over 66% and peaking at over 1,000 active groups in 2011 (see Brown 2009; Cooper 2010; Potok 2015). Although down since 2011, the number of active groups increased from 892 in 2015 to 917 in 2016 (Potok 2017). Measuring online hate groups does not fully capture the universe of online hate, however. Indeed, today, the number of individuals maintaining websites or espousing hate online who are not affiliated, or only loosely affiliated, with hate groups outstrips the number of hate groups (Potok 2015).

This surge in online hate naturally translates into increased exposure to such material. Recent work found that the number of Americans between the ages of 15 and 30 who were exposed to online hate increased from 53% in 2013 to 63% in 2015 (compare Hawdon, Oksanen, and Rasanen 2015; Costello, Haw- don, and Ratliff 2016; also see Ybarra, Mitchell, and Korchmaros 2011). A more recent survey found that the number jumped to 70% in 2016 (Hawdon and Costello 2017). The rise coincides with the evolution of online interactivity, which allows for quick and easy dissemination of hate through blogs, social media platforms, and mass emails, among other conduits.

Although most people who see hate material are not noticeably harmed by it—for instance, some actively seek it out, and others scroll past it without much notice (Gerstenfeld, Grant, and Chiang 2003; McNamee, Peterson, and Pena 2010)—others are affected. In rare cases, exposure has been linked to incidences of violence (Federal Bureau of Investigation 2011; Freilich et al. 2014; The New America Foundation International Security Program 2015). But, although these cases are rare, experts are increasingly fearful that the link between hate speech and action is becoming progressively more tangible. This is especially true as concerns over conspiracy theories and fake news grow, and fringe thinking and the incitement of hate are mainstreaming (Faiola and Kirchner 2016). In addition, given the link between exposure to online hate and mood swings, anger, fear, and diminished well-being (Keipi et al. 2017; Tynes 2006; Tynes, Reynolds, and Greenfield 2004), being the target of hate based on one’s sexual orientation could be especially damaging because of the destructive impact it can have on one’s core identity.

Hate and the LGBTQ Community

A sizable literature examines the prevalence of hate crimes targeting members of the LGBTQ community. Herek (2009), using a national sample of gay, lesbian, and bisexual adults, found that roughly half of those examined reported experiencing verbal harassment, and approximately 20% were victims of a crime based on their sexual orientation. Other work largely echoes these results. For instance, Berrill and Herek (1992), focusing on a series of published and unpublished studies concerning LGBT-targeted hate and criminality between 1977 and 1991, found that 80% of respondents reported being verbally harassed, and 44% had been threatened. Smaller percentages reported being the victim of aggravated assault (9%), simple assault (17%), or vandalism (19%) based on their sexuality. Herek and colleagues (1999), examining lesbian, gay, and bisexual adults in

Sacramento, California, found that 13% of gay men, 7% of lesbians, 11% of bisexual men, and 5% of bisexual women had experienced a simple or aggravated assault based on their sexual orientation, whereas smaller percentages reported a sexual assault based on their sexuality.

Looking at young gay and bisexual men in the Southwest, Huebner, Rebchook, and Keg- eles (2004) found that 5% of respondents reported being the victim of physical violence based on their sexuality in the past six months. A national sample of young lesbian, gay, and bisexual men showed that aggravated assault (9%), simple assault (18%), sexual assault (22%), and threats with attack (44%) were relatively common. Further, a study in New York City and surrounding areas focusing on youths who identified as sexual minorities demonstrated that 11% of respondents were subject to physical violence, 9% reported sexual assault, and 78% said they had been subject to verbal threats of harassment, all based on their sexual orientation (D’Augelli, Grossman, and Starks 2006). D’Augelli and Grossman (2001) found that older lesbian, gay, and bisexual individuals are at risk as well. Lifetime occurrence rates showed that 16% suffered physical assault, 7% were victims of sexual assault, and 29% were threatened with violence.

Social Identity Theory

All crimes have a negative impact on their victims. But incidences of hate are often argued to have a more pronounced detrimental impact on the attacked (Herek, Gillis, and Cogan 1999; Herek et al. 1997). In a comparison of victims of bias and non-bias crimes, Herek and colleagues (1999) found that lesbian and gay victims showed significantly more symptoms of depression, post-traumatic stress disorder (PTSD), anxiety, and anger, compared to victims of analogous non-bias crimes. Additionally, gay and lesbian victims viewed the world as more unsafe, had a diminished view of the goodness of others, questioned their own abilities, and were more likely to attribute their attacks to their sexual orientation, all compared to victims of non-bias crimes. Gay and lesbian victims also reported longer recovery times as their symptoms of anger, depression, and PTSD lingered for years.

Hate speech is so powerful precisely because it attacks a person for who they are. According to Social Identity Theory, our self-worth and self-determination are largely tied to our cognitive and affective bond to particular social categories (Boeckmann and Liew 2002; Tajfel and Turner 2004). A person’s self-esteem, in part, is bonded to membership with a social group and how that membership is viewed by others (Luhtanen and Crocker 1992). Turner (1999) notes that social identity becomes most salient when boundaries are clearly drawn between members of a group and non-members. Hate speech does exactly that, not only drawing borders between members of a group and nonmembers, but also simultaneously devaluing members of the targeted group. Further, hate erodes the positive aspects of group membership, chipping away at the self-esteem derived from group membership.

According to Garnets and associates (1990), a positive self-identity is crucial for gay, lesbian, and bisexual individuals to cope with stresses that result from societal prejudices. Hate, however, can alter one’s sense of self. In particular, research finds that being the victim of a hate crime causes the victim’s core identity to become linked with a sense of vulnerability that naturally follows any victimization (Norris and Kani- asty 1991). This means that instead of identifying one’s sexual identity with feelings of love and acceptance, victims of hate-motivated offenses instead correlate their sexuality with pain and danger (Garnets, Herek, and Levy 1990).

LGBTQ Experiences in the South and Rural Areas

Rightly or wrongly, the South is often portrayed as a region that struggles with accepting diversity. The LGBTQ civil rights movement has faced major hurdles in the South, and public opinion polls consistently show higher rates of sexual prejudice in the South (Baunach, Burgess, and Muse 2009; Herek 1994. Smith 1997; Swank, Fahs, and Frost 2013). In 2014, the MSNBC television network commissioned a study of state LGBT climates. Examining state policies on marriage equality, adoption codes, and religious freedom restoration codes, the worst performing states were Alabama, Florida, Kentucky, Louisiana, Mississippi, Missouri, Tennessee, and Texas, all former Confederate states (MSNBC 2014). Given findings such as these, it is unsurprising that studies show verbal abuse based on sexuality is more common amongst gay men in Kentucky (Tewksbury et al. 1999).

Traditionally, sodomy laws have been the weapon of choice to use against LGBTQ individuals by criminalizing same gender sexual encounters (Leyland 2002). Even after the Supreme Court ruled that sodomy statutes were unconstitutional in Lawrence v. Texas (539 U.S. 186), 12 states have refused to repeal their sodomy laws from the books, 8 of which are located in the American South (Associated Press 2014). A 2014 attempt to repeal Louisiana’s sodomy law in 2014 failed by a 66-27 vote in the state’s House of Representatives (O’Donoghue 2014).

Since the Supreme Court decision in Obergell v. Hodges (576 U.S. 2015), which legalized gay marriage across the United States, Southern opposition to LGBTQ rights has intensified. Immediately following the ruling, a Kentucky county clerk, in defiance of the court, was jailed for refusing to issue marriage licenses to same- sex couples (Higdon and Somashekhar 2015). The following year North Carolina received national attention after passing HB2, which nullified local LGBTQ anti-discrimination ordinances and limited transgender bathroom use, an action criticized nationally by LGBTQ groups and many large corporations (Gordon, Price, and Peralta 2016). Recently, a movie theater in Alabama refused to show Disney’s Beauty and the Beast because a character is openly gay (Barnes and Deb 2017). In this context, it is unsurprising that the Trevor Project, a telephone hotline for LGBT individuals contemplating suicide, receives more calls from the South than other region in the county (Trevor Project 2016).

In addition to region, research shows that locality—urban versus rural—can be an influential factor in the LGBTQ climate. The tight-knit, often religiously rooted structure of rural communities can create hostile environments for members of the LGTB community (Smith 1997). Studies have found that individuals in non-metropolitan LGBT areas experience high levels of homophobia and family estrangement that increase levels of self-isolation and guardedness about sexual orientation (Boulden 2001; Connolly and Leedy 2006; Lee and Quam 2013), a phenomenon Connolly and Leedy (2006, p. 16) refer to as “Don’t ask. Don’t tell.”

There are numerous reasons for the feelings of unease felt by many LGBTQ rural-dwellers. Rural areas tend to prize culture homogeneity, religiosity, and “traditional” values. Urban areas tend to be hubs of newness, and thus largely disregard authoritarian morality. Urbanites are forced to respond to heterogeneous lifestyles in a way that rural residents are not. City-dwellers are also more apt to hold liberal views, be more educated, and less religious, traits associated with an acceptance of diversity, including diversity of sexual orientation (Moore and Vanneman 2003; Van Dyke, Soule, and Windom 2001). Unsurprisingly, this phenomenon results in many LGBTQ individuals migrating from rural areas to bigger cities. In urban areas, LGBTQ individuals struggling with issues of sexual orientation are able to meet other LGBTQ individuals facing similar struggles and come together for group support (Leyland 2002). However, this path is not feasible for many LGBTQ individuals who lack resources or cannot leave rural areas due to family or communal ties.

When asked about rural living, LGBTQ individuals portray it negatively, describing it as inhospitable (Barton 2010; Gray 2009; Kazyak 2011; McCarthy 2000). This leads many to hide their sexual identities and feel lonely, isolated, and fearful. Recent work by Swank and colleagues (2013) demonstrates that rural sexual minorities are more likely to face various forms of enacted stigma if they are open about their sexuality, compared to sexual minorities in similar locales who do not disclose their identity. Poon and Saewyc (2009), studying Canadian LGB adolescents, found that rural youths were more likely to be verbally teased and physically assaulted, relative to their urban counterparts. Similarly, LGB high school students in urban areas faced fewer homophobic remarks and less sexual harassment related to their sexual identity, compared to urban-dwelling students (Kosciw, Greytak, and Diaz 2009), and rural lesbian mothers are more likely to experience public harassment and rejection in relation to urban lesbian moms (Puckett et al. 2011).

Methods and Data

This study seeks to understand factors related to the targeting of online hate based on sexuality. We use logistic regression because we are analyzing a binomial dependent variable: if an individual has or has not been a target of online hate based on their sexual orientation. The effect of independent variables is reported as odds ratios, which show relative changes in the odds of an outcome when an independent variable’s value is increased by one unit, holding all other effects constant.

Sample

The sample consists of 968 internet users aged 15 to 36 and was collected during the week of January 28, 2015, from demographically balanced panels of people who voluntarily agreed to participate in research surveys. Survey Sample International (SSI) recruits potential participants through permission-based techniques such as random digit dialing and banner ads. SSI sent email invitations to a sample of panel members ages 15 to 36, stratified to reflect the US population on age, gender, and geographic region. SSI provides various incentives to respondents for participating in their surveys. The ages 15 to 36 were selected because these data are from a study of exposure to online hate materials designed in part to match comparative samples from earlier research conducted in several European nations (e.g., Rasanen et al. 2016).

Demographically balanced online panels protect against bias in online surveys because screening can eliminate respondents and panelists who have previously participated (Evans and Matluir 2005; Wansink 2001). Moreover, the recruitment and selection processes, the use of pre-panel interviews, and incentives increase the validity of responses because those who volunteer to be in the panel tend to be more serious about answering the questions (see Wansink 2001).

Dependent Variable

Target of Online Hate Based on Sexual Orientation

Our dependent variable asks respondents if respondents have been the target of online hate based on their sexual orientation. Respondents were first asked, “Have you ever personally been the target of hateful or degrading material online?” Those who were victimized were asked to specify what the hateful or degrading material that targeted them pertained to. We found that 5.9% of our respondents indicated that they have been targeted because of their sexuality at some point in their life. This was one of the most common types of targeting amongst our respondents, with only targeting based on race/ethnicity, religious beliefs, and appearance being more common.

Independent Variables

Past work shows the applicability of RAT to cybercrime (Costello, Hawdon, and Ratliff 2016; Costello et al. 2016; Bossier and Holt 2009: Holt and Bossier 2013; Holtfreter, Reisig, and Pratt 2008; Leukfeldt and Yar 2016; Marcum, Higgins, and Ricketts 2010; Reyns and Henson 2015; Reyns, Henson, and Fisher 2015; van Wilsem 2011). Thus, our independent variables measure the core aspects of RAT, which are exposure to potential offenders, target suitability, and guardianship. We also control for sociodemographic characteristics, including where individuals live, in the full model.

Exposure to Online Hate

All else equal, we expect individuals who are more regularly exposed to online hate to be more likely victims. We therefore control for online habits that affect exposure patterns. First, we asked respondents how many hours they spend online per day. The variable response set ranges from 1, “less than one hour per day,” to 6, “ten or more hours per day.” Respondents spend between 3 and 5 hours online per day, on average. We expect to find a positive relationship between time online and targeting, surmising that individuals who spend more time online will simply have more opportunities to be targeted.

Second, we asked respondents to tell us about their social media use. Specifically, respondents were asked to indicate whether they use a series of common platforms, including Facebook, YouTube, Twitter, Google+ , Habbo, Wikipedia, MySpace, Ning, MyLife, Tumblr, Live Journal, newspaper message boards, general message boards, image boards (e.g., 4chan), instant messengers (e.g., Windows Live Messenger), online role-playing games (e.g., World of Warcraft), photo-sharing services (e.g., Instagram, Pinterest), blogs, Skype, anonymous networks (e.g., Tor), and email. The variable sums the social networking services (SNS) respondents used in the three months prior to being surveyed. Although the original variable ranged from 0 to 22, it was highly skewed. We therefore recoded the variable to range from 0, corresponding to “none,” to 12, corresponding to “12 or more sites/ser- vices.” Nearly 93% of the sample used 11 or fewer sites or services and recoding the variable did not influence the results in any substantive way. Like time spent online, we anticipate that individuals who use more SNS will be more apt to face- targeting by virtue of increasing exposure.

We also assess if respondents visited hostile online environments using a composite of three indicators. The factorability of all three measures demonstrated that each measure shares common variance with the other items. The first indicator asked respondents how often they see people being hateful on SNS. Responses range from 1 to 4, with 1 corresponding to “never,” and 4 corresponding to “frequently.” The second measure, utilizing the same scale, asked respondents how often they see people using language, images, or humor on SNS they find offensive. The third measure asked respondents if they believe people are “mostly kind” or “mostly unkind” to others on SNS, or if “it depends” on the situation. Individuals who visit hostile online environments with more regularity are hypothesized to face a greater likelihood of being targeted. Indeed, exposure to hate is a strong predictor of targeting, both online and offline (Bossier and Holt 2009; Holtfreter et al. 2010; Jennings, Piquero, and Reingle 2012).

Target Suitability

There are a number of ways that one’s actions online can potentially magnify or diminish their likelihood of being a target for purveyors of hate. One way to potentially increase the likelihood of targeting is to engage in online confrontations, either in an attempt to intensify or dees- calate them. We use two measures to estimate engagement in confrontations. First, we asked respondents who witnessed hate online how often they either told the person who is being offensive to stop or defended the person who is being attacked. These two items were combined into a composite indicator approximating enacting informal social control. Both measures have response sets ranging from 1, referring to “never,” to 4, corresponding to “frequently.” Sizable shares of respondents said they never tell hateful individuals to stop their behavior (28.3%) or defend the attacked (20.2%). Frequently intervening, either to tell the person to cease their behavior (10.2%), or by defending the target of the attacks (13.7%), was fairly uncommon. Respondents were much more likely to say that they engaged in attempts of informal social control either once in a while or sometimes. Although the attempt of the interveners in this instance is to mediate a conflict, it is possible that the intervention will, in turn, make the potential arbitrator the target of hate. By simply engaging the offender, an individual is drawing attention to themselves, thereby making themselves a potential victim (Costello, Hawdon, and Cross 2017).

Second, we assess if respondents were antagonistic online. More specifically, we asked how

often, if ever, they joined in when encountering hate online. We use a 4-point scale ranging from 1, “never,” to 4, “frequently.” Almost three- quarters (73%) of our sample said they never engage in this type of behavior, whereas only 3.1% said they do so frequently. Roughly 24% of respondents said they did so sometimes or once in a while. We expect more antagonistic individuals will be more likely to become targets themselves, in part as a result of engaging in a confrontation, but also because they are in virtual proximity to hateful material.

We also asked respondents if they discussed private matters online by having them rate on a 10-point scale how easy they find it to discuss private matters online when others do not know who they are. A score of 1 indicated the statement was “not very true of me,” and a score of 10 indicated that it was “very true of me.” Approximately 11% of respondents recorded a score of 10, and 15.3% recorded a 1. Slightly over 23% responded with a more neutral response of 5 or 6. We hypothesize that those who discuss private matters online more willingly are more likely to become targets of hate. Discussing private matters online can provide fodder for would-be haters, and thus doing so opens one to the possibility of being targeted.

Guardianship

The third aspect of RAT concentrates on social bonds that act as capable guardians. We use three measures to estimate online and offline social bonds. One measure asked respondents how close they are to an online community to which they belong. Closeness to an online community is measured on a 5-point scale. A response of 5 indicates that individuals “feel very close to an online community to which they belong,” whereas a value of 1 indicates that they feel “not close at all” to such a community. Most of the survey respondents reported a moderate level of closeness to an online community, with nearly 69% reporting a score of 2, 3, or 4. Only 9.1% reported feeling very close to an online community. This item has been used in previous studies of online victimization to assess if online attachments serve as a protective factor (see

Oksanen et al. 2014). Thus, we expect individuals with stronger ties to an online community to be less likely to be targeted.

We use two measures to examine social bonds offline. The first is an index created from two measures—one concerning closeness to family, and the other regarding closeness to friends. Like our measure of closeness to an online community, this indicator uses an identical 5-point Likert scale. Over 80% of respondents reported a 4 or 5 regarding closeness to their family, and 77% responded similarly when asked about closeness to friends. Only 5% of respondents reported a 1 or 2 regarding closeness to family, whereas only 5.9% responded similarly regarding closeness to friends. Like online social bonds, we expect close family and friendship bonds to reduce the likelihood of being targeted.

Our second measure of offline guardianship is a dummy variable that indicates whether respondents lived alone as people who live alone most likely have lower levels of guardianship (Reyns, Henson, and Fisher 2016). Only 11.5% of respondents reported living alone. We expect them to have higher rates of being targeted by hate online.

Place of Residence

We use two measures to gauge respondents’ place of residence to assess if residence affects their likelihood of being targeted by online hate pertaining to sexual orientation.

We control for region of the country using a dummy variable that codes 16 Southern states as 1 and all other states as 0, following the U.S. Census Bureau regional breakdown. We use a dummy variable to capture whether an individual lived in a rural locale. Rural areas are defined as open country areas with less than 50,000 residents. We hypothesize that individuals living in the South or rural areas will have a greater likelihood of being targeted by online hate related to sexual orientation. Even though the internet allows for interactions that transcend boundaries, all else being equal, individuals are more prone to interact online, positively or negatively, with those who they interact with in the offline world (see Subrahmanyam et al. 2008; Valkenburg and Peter 2007), and these individuals will most likely be those who are in geographic proximity to them.

Sociodemographics

Finally, we control for sociodemographic variables, including gender, age, and a dummy variable for race/ethnic minority status. Although others have found that these factors are unrelated to targeting in general (see Nasi et al. 2014; Rasanen et al. 2016), it is possible that these characteristics pattern specific forms of targeting, such as that based on sexuality.

Findings

We use a two-model sequence: the first model controls for variables that proxy the three components of RAT, and the second model adds indicators for place of residence and other sociodemographic variables to the equation.

Model 1 shows that using more SNS leads to an increased likelihood of targeting based on sexuality (OR = 1.18, p < .001). In fact, respondents who report using more social networking platforms are 1.18 times as likely to be targeted. This finding aligns with expectations regarding increased internet usage and targeting. However, spending more time online does not significantly affect our outcome of interest, nor does visiting hostile online environments.

Engaging in confrontations, as hypothesized, is related to targeting as well. Interestingly, trying to enact social control online by defending others who are being targeted increases the chance that the defender will now become a target (OR = 1.76, p < .01). More precisely, defenders are 1.76 times as likely to be targeted, relative to those who do not confront hate online. Similarly, those who see hate and join in the hate are 1.55 times more likely to then become a target themselves (OR = 1.55, p < .01). These findings provide powerful evidence that both positive and negative engagement online can lead to targeting. Discussing private matters online does as well, as those who do so are 1.14 times more likely to be targeted by hate based on their sexuality (OR =1.12, p < .05). Counter to our hypotheses, we find no evidence that social bonds—either online or offline— affect targeting based on sexual orientation.

The second model demonstrates that, whereas gender, age, and minority status are unrelated to targeting based on sexuality, living in the South and living in rural areas both increase one’s chances of being targeted. In fact, these are the two strongest effects in the model. Respondents in the South were nearly three times as likely to be targeted compared to individuals living in other geographic regions (OR = 2.92, p < .001). Additionally, respondents living in rural areas are more than two times as likely to be targeted based on their sexual orientation, relative to those living in the suburbs, or small, medium, or large cities (OR = 2.25, p < .05). Both of these findings align with our expectations. The results from the first model are generally parallel in the second model. There is no change in significance, and only slight change in the magnitude of effects for the RAT variables. We also tested for a possible interaction effect between living in the South and living in rural areas, positing that individuals who live in rural areas in the South might be particularly susceptible to being victimized. However, the effect was nonsignificant, and we therefore do not report it.

Discussion

Hate speech can profoundly affect those who are targeted. Past work shows that the effects of hate run the gamut from mundane to severe. Hate speech derives its power from its ability to devalue one’s very identity. Not only that, but hate speech can make individuals question their lifestyle, social networks, and even cause victims to blame themselves for their victimization. These ill effects are amplified for individuals who feel ostracized, either from their family or society at large (Herek 1992), which is often the case with sexual minorities. Indeed, research has shown an unsupportive environment increases the suicide risk by 20% (Hatzenbuehler 2011).

Several of our hypotheses received at least partial support. Echoing previous findings concerning online targeting of hate, we find that certain internet habits affect the likelihood of being the target of hate based on sexuality. We found that SNS usage, but not time online or browsing behavior, increases targeting. Being engaging or antagonistic online also increased being targeted. In fact, seeing hate and defending the attacked, seeing hate and joining in, and discussing private matters online all led to a greater likelihood of being targeted by hate based on sexual orientation. Our hypotheses regarding online and offline guardianship were not supported. Although all of the effects are in the hypothesized direction, living alone and having close bonds to family, friends, or an online community all fail to reach significance in either model. This is not entirely surprising, though, as the efficacy of guardianship in an online setting has received less support than the other elements of RAT (Bossier and Holt 2009; Choi 2008; Leukfeldt and Yar 2016; Reyns 2015).

Our third and fourth hypotheses both receive strong support: those living in the South were targeted nearly three times more as those living elsewhere, and those living in rural areas were targets of anti-LGBT online hate twice as often as those living in other locales. Both of these outcomes align with past findings showing increased rates of harassment, bullying, and violence associated with sexuality in the South and rural areas.

Several explanations could account for the regional effects found in this study, ranging from disparities in religiosity between regions to a desire to hold tight to “traditional” values in the South and rural areas. It is worthwhile evaluating these findings in the context of Contact Theory (Allport 1958; Pettigrew 1998). Contact Theory suggests that amiable relationships develop between members of majority and minority groups under certain conditions. Chief among them, intergroup contact has been found to lower intergroup prejudice (Allport 1958; Pettigrew 2005). Contact Theory is useful for explaining various types of negative attitudes toward marginalized groups (Krahe and Altwas- ser 2006; Lee, Farrell, and Link 2004; McClelland and Linnander 2006; Reinke et al.

2004), including gay and lesbian individuals (Herek 2000; Schope and Eliason 2000). Studies show that individuals who report knowing someone who is gay express more positive attitudes toward lesbian and gay individuals than do those who do not report such contacts (Berk- man and Zinberg 1997; Herek 2000; Herek and Glunt 1993). Other work shows that having a close relationship to a sexual minority diminishes prejudice against LGBT individuals, as does having more LGBT contacts (Basow and Johnson 2000; Finlay and Walther 2003; Herek and Capitanio 1995, 1996).

Individuals residing in the South or rural areas are perhaps less likely to have numerous and intimate contacts with LGBTQ individuals. This could, in part, explain the increased rates of online victimization based on sexuality in these areas. In rural areas, and many parts of the South, this could simply be the result of sparsity. Indeed, the land-to-human ratio is high in rural areas, as well as many parts of the South. This by nature lessens all human contacts. Moreover, research shows that sexual minorities are more prone to hide their sexuality in the South and rural locales. Again, this opaqueness lessens the chance that straight individuals will knowingly have contact with a member of a sexual minority group.

Although these explanations of our findings are certainly plausible and, we believe, probable, we recognize these are contingent on an untested assumption. As the internet is truly the “worldwide web,” it does not really make sense that region and residential location would influence targeting. That is, at least in nations that do not restrict access, internet sites are equally available to everyone with access to the internet regardless of their location. For region to have an effect in the online world, it is likely that online associations significantly overlap with offline associations. If this is indeed the case, individuals living in the South or in rural areas would be more likely to interact with people holding anti-LGBT attitudes both online and offline, all else being equal. Although previous research finds there is considerable overlap between offline and online networks, our data do not allow us to establish this empirically in our sample. This is therefore a limitation of our research.

Limitations

Our study has additional limitations that merit consideration. First, our sample is limited to individuals between the ages of 15 and 36. The decision to limit our sample was strategic as research shows young people spend more time online and are therefore more likely targets of online hate. Even so, this places limits on our sample. We also face a limitation that coincides with all survey-based research. Namely, it is possible that individuals who chose to participate in our survey differ from individuals who chose not to participate. Thus, though our sample is demographically balanced, allowing for general claims to be made regarding our findings, we cannot be sure that our sampling procedure does not suffer from other biases.

Our dependent variable also has limitations. It asks respondents if they have ever been the target of online hate based on sexuality. Thus our study is correlational, which hampers our ability to make strong claims regarding causal relationships. We believe that it is certainly possible that many of our independent variables temporally precede our outcome, but we cannot know this with certainty. Finally, because our dependent variable is based on a subjective judgment, our ability to generalize our findings is limited.

Conclusions

The internet can cause great harm to vulnerable populations, but it can also be a source of great comfort. Therein lies the paradox. For those living in inhospitable environments, online communication and social networking can be a boon. They allow a person struggling with his or her identity to reach out to others experiencing similar feelings. The web opens the world so these individuals realize they are not alone; it also allows them to share coping strategies. Ybarra and associates (2015) found

LGBT youth were more likely to have online friends, and those friends are a vital link in their support systems. Yet, cyberspace can be a dangerous place. The amount of online hate is growing, and there is little reason to believe that trend will slow or reverse. Stigmatized groups are finding themselves increasingly the target of a growing population of hatemon- gers. As noted earlier, the consequences of this hate can be dire. This raises an important question—what, if anything, can be done to curb anti-LGBTQ hate?

There is little that the government can do to regulate hate speech online, as primacy is afforded the First Amendment in the United States (Hawdon, Oksanen, and Pekka 2016). Formal social control mechanisms, such as firewalls, antivirus programs, filtering, and blocking software, have proven ineffective at curtailing online hate as well (Bossier and Holt 2009; Fleming et al. 2006). Additionally, informal social control can, at times, have the opposite of the intended effect. Even when an individual intervenes in a situation involving hate with the intent of aiding the victim, hate is often multiplied, not dismissed. The news is not all bad, though. There is evidence that online collective efficacy, although difficult to foster in cyberspace due to the internet’s impersonal and dispersed nature, can reduce online hate by sending the message to would- be haters that such behavior is not tolerated in that environment (Costello, Hawdon, and Cross 2017). Thus, the likelihood of being the target may be reduced, at least somewhat, if individuals employ strategic internet usage patterns.

Discussion Questions

  • 1. How did the researchers operationalize each dimension of the routine activities perspective?
  • 2. Why is it that we cannot be sure that people were victimized online because of their sexuality?
  • 3. How did the researchers’ sampling decisions possibly impact the results that they found?

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