Illusory interparty disagreement: Partisans agree on what hate speech to censor but do not know it.

Whether and when to censor hate speech are long-standing points of contention in the US. The latest iteration of these debates entails grappling with content regulation on social media in an age of intense partisan polarization. But do partisans disagree about what types of hate speech to censor on social media or do they merely differ on how much hate speech to censor? And do they understand out-party censorship preferences? We examine these questions in a nationally representative conjoint survey experiment (participant N = 3,357; decision N = 40,284). We find that, although Democrats support more censorship than Republicans, partisans generally agree on what types of hate speech are most deserving of censorship in terms of the speech's target, source, and severity. Despite this substantial cross-party agreement, partisans mistakenly believe that members of the other party prioritize protecting different targets of hate speech. For example, a major disconnect between the two parties is that Democrats overestimate and Republicans underestimate the other party's willingness to censor speech targeting Whites. We conclude that partisan differences on censoring hate speech are largely based on free speech values and misperceptions rather than identity-based social divisions.

Disrupting hate: The effect of deplatforming hate organizations on their online audience.

How does removing the leadership of online hate organizations from online platforms change behavior in their target audience? We study the effects of six network disruptions of designated and banned hate-based organizations on Facebook, in which known members of the organizations were removed from the platform, by examining the online engagements of the audience of the organization. Using a differences-in-differences approach, we show that on average the network disruptions reduced the consumption and production of hateful content, along with engagement within the network among periphery members. Members of the audience closest to the core members exhibit signs of backlash in the short term, but reduce their engagement within the network and with hateful content over time. The results suggest that strategies of targeted removals, such as leadership removal and network degradation efforts, can reduce the ability of hate organizations to successfully operate online.

Is hate speech detection the solution the world wants?

The machine learning (ML) research community has landed on automated hate speech detection as the vital tool in the mitigation of bad behavior online. However, it is not clear that this is a widely supported view outside of the ML world. Such a disconnect can have implications for whether automated detection tools are accepted or adopted. Here we lend insight into how other key stakeholders understand the challenge of addressing hate speech and the role automated detection plays in solving it. To do so, we develop and apply a structured approach to dissecting the discourses used by online platform companies, governments, and not-for-profit organizations when discussing hate speech. We find that, where hate speech mitigation is concerned, there is a profound disconnect between the computer science research community and other stakeholder groups-which puts progress on this important problem at serious risk. We identify urgent steps that need to be taken to incorporate computational researchers into a single, coherent, multistakeholder community that is working towards civil discourse online.

Hate crime supporters are found across age, gender, and income groups and are susceptible to violent political appeals.

Hate crime is a pervasive problem across societies. Though perpetrators represent a small share of the population, their actions continue in part because they enjoy community support. But we know very little about this wider community of support; existing surveys do not measure whether citizens approve of hate crime. Focusing on Germany, where antiminority violence is entrenched, this paper uses original surveys to provide systematic evidence on the nature and impacts of hate crime support. Employing direct and indirect measures, I find that significant shares of the population support antirefugee hate crime and that the profile of supporters is broad, going much beyond common perpetrator types. I next use a candidate choice experiment to show that this support has disturbing political consequences: among radical right voters, hate crime supporters prefer candidates who endorse using gun violence against refugees. I conclude that a significant number of citizens empower potential perpetrators from the bottom-up and further legitimize hate crime from the top-down by championing violence-promoting political elites.

On the rise of fear speech in online social media.

Recently, social media platforms are heavily moderated to prevent the spread of online hate speech, which is usually fertile in toxic words and is directed toward an individual or a community. Owing to such heavy moderation, newer and more subtle techniques are being deployed. One of the most striking among these is fear speech. Fear speech, as the name suggests, attempts to incite fear about a target community. Although subtle, it might be highly effective, often pushing communities toward a physical conflict. Therefore, understanding their prevalence in social media is of paramount importance. This article presents a large-scale study to understand the prevalence of 400K fear speech and over 700K hate speech posts collected from Gab.com. Remarkably, users posting a large number of fear speech accrue more followers and occupy more central positions in social networks than users posting a large number of hate speech. They can also reach out to benign users more effectively than hate speech users through replies, reposts, and mentions. This connects to the fact that, unlike hate speech, fear speech has almost zero toxic content, making it look plausible. Moreover, while fear speech topics mostly portray a community as a perpetrator using a (fake) chain of argumentation, hate speech topics hurl direct multitarget insults, thus pointing to why general users could be more gullible to fear speech. Our findings transcend even to other platforms (Twitter and Facebook) and thus necessitate using sophisticated moderation policies and mass awareness to combat fear speech.

Hidden resilience and adaptive dynamics of the global online hate ecology.

Online hate and extremist narratives have been linked to abhorrent real-world events, including a current surge in hate crimes1-6 and an alarming increase in youth suicides that result from social media vitriol7; inciting mass shootings such as the 2019 attack in Christchurch, stabbings and bombings8-11; recruitment of extremists12-16, including entrapment and sex-trafficking of girls as fighter brides17; threats against public figures, including the 2019 verbal attack against an anti-Brexit politician, and hybrid (racist-anti-women-anti-immigrant) hate threats against a US member of the British royal family18; and renewed anti-western hate in the 2019 post-ISIS landscape associated with support for Osama Bin Laden's son and Al Qaeda. Social media platforms seem to be losing the battle against online hate19,20 and urgently need new insights. Here we show that the key to understanding the resilience of online hate lies in its global network-of-network dynamics. Interconnected hate clusters form global 'hate highways' that-assisted by collective online adaptations-cross social media platforms, sometimes using 'back doors' even after being banned, as well as jumping between countries, continents and languages. Our mathematical model predicts that policing within a single platform (such as Facebook) can make matters worse, and will eventually generate global 'dark pools' in which online hate will flourish. We observe the current hate network rapidly rewiring and self-repairing at the micro level when attacked, in a way that mimics the formation of covalent bonds in chemistry. This understanding enables us to propose a policy matrix that can help to defeat online hate, classified by the preferred (or legally allowed) granularity of the intervention and top-down versus bottom-up nature. We provide quantitative assessments for the effects of each intervention. This policy matrix also offers a tool for tackling a broader class of illicit online behaviours21,22 such as financial fraud.

Asian Hate, religion, and perceived changes in physical health: exploring the flip side of minority stress during the COVID-19 pandemic.

OBJECTIVES: Prior research suggests that racism is associated with adverse mental health outcomes for Asians in the United States. Relatively less research has been conducted to examine the effects of racism on physical health, particularly the changes in physical health among Asians and Asian Americans. This study aims to fill in this gap in prior research. DESIGN: Survey was conducted via Qualtrics in March 2023. A panel sample of 356 Asian and Asian American adults from across the US was collected. Ordinary Least Squares Regression was employed to examine the interrelationships among racism, religion, and perceived changes in physical health during the COVID-19 pandemic. RESULTS: Contrary to conventional wisdom, more frequent experience with blatant racism was associated with a perceived improvement in physical health after controlling for subtle racism, anxiety, acculturation, and various sociodemographic variables. Interestingly, this robust relationship was more significant among Asians who attended religious services more frequently. Additional three-way interactions revealed that the interaction between blatant racism and religious service attendance on perceived changes in physical health was more significant for US-born Asians and Asians of Indian or Japanese ethnicity. CONCLUSION: Racism exerts a significant influence on physical health outcomes among Asians and Asian Americans. However, this relationship was contingent upon the specific aspect of racism and intersected with religiosity, acculturation, and ethnic identity.

Empathy-based counterspeech can reduce racist hate speech in a social media field experiment.

Despite heightened awareness of the detrimental impact of hate speech on social media platforms on affected communities and public discourse, there is little consensus on approaches to mitigate it. While content moderation-either by governments or social media companies-can curb online hostility, such policies may suppress valuable as well as illicit speech and might disperse rather than reduce hate speech. As an alternative strategy, an increasing number of international and nongovernmental organizations (I/NGOs) are employing counterspeech to confront and reduce online hate speech. Despite their growing popularity, there is scant experimental evidence on the effectiveness and design of counterspeech strategies (in the public domain). Modeling our interventions on current I/NGO practice, we randomly assign English-speaking Twitter users who have sent messages containing xenophobic (or racist) hate speech to one of three counterspeech strategies-empathy, warning of consequences, and humor-or a control group. Our intention-to-treat analysis of 1,350 Twitter users shows that empathy-based counterspeech messages can increase the retrospective deletion of xenophobic hate speech by 0.2 SD and reduce the prospective creation of xenophobic hate speech over a 4-wk follow-up period by 0.1 SD. We find, however, no consistent effects for strategies using humor or warning of consequences. Together, these results advance our understanding of the central role of empathy in reducing exclusionary behavior and inform the design of future counterspeech interventions.

From Tweets to Streets: Observational Study on the Association Between Twitter Sentiment and Anti-Asian Hate Crimes in New York City from 2019 to 2022.

BACKGROUND: Anti-Asian hate crimes escalated during the COVID-19 pandemic; however, limited research has explored the association between social media sentiment and hate crimes toward Asian communities. OBJECTIVE: This study aims to investigate the relationship between Twitter (rebranded as X) sentiment data and the occurrence of anti-Asian hate crimes in New York City from 2019 to 2022, a period encompassing both before and during COVID-19 pandemic conditions. METHODS: We used a hate crime dataset from the New York City Police Department. This dataset included detailed information on the occurrence of anti-Asian hate crimes at the police precinct level from 2019 to 2022. We used Twitter's application programming interface for Academic Research to collect a random 1% sample of publicly available Twitter data in New York State, including New York City, that included 1 or more of the selected Asian-related keywords and applied support vector machine to classify sentiment. We measured sentiment toward the Asian community using the rates of negative and positive sentiment expressed in tweets at the monthly level (N=48). We used negative binomial models to explore the associations between sentiment levels and the number of anti-Asian hate crimes in the same month. We further adjusted our models for confounders such as the unemployment rate and the emergence of the COVID-19 pandemic. As sensitivity analyses, we used distributed lag models to capture 1- to 2-month lag times. RESULTS: A point increase of 1% in negative sentiment rate toward the Asian community in the same month was associated with a 24% increase (incidence rate ratio [IRR] 1.24; 95% CI 1.07-1.44; P=.005) in the number of anti-Asian hate crimes. The association was slightly attenuated after adjusting for unemployment and COVID-19 emergence (ie, after March 2020; P=.008). The positive sentiment toward Asian tweets with a 0-month lag was associated with a 12% decrease (IRR 0.88; 95% CI 0.79-0.97; P=.002) in expected anti-Asian hate crimes in the same month, but the relationship was no longer significant after adjusting for the unemployment rate and the emergence of COVID-19 pandemic (P=.11). CONCLUSIONS: A higher negative sentiment level was associated with more hate crimes specifically targeting the Asian community in the same month. The findings highlight the importance of monitoring public sentiment to predict and potentially mitigate hate crimes against Asian individuals.

Desensitization to hate speech: Examination using heart rate measurement.

Exposure to hate speech (HS) leads to desensitization of listeners. Yet, most evidence of this process has been obtained using self-report measures. In this paper, we examined desensitization to HS using an unobtrusive, psychophysiological measure. In an experimental electrocardiogram study (N = 56), we observed heart rate (HR) deceleration after reading comments that contained HS. This suggested a substantive psychophysiological reaction of participants to hateful comments. However, such HR deceleration was not observed among participants preexposed to HS. People exposed to hateful comments thus appeared to show different HR responses to HS compared to people who were not previously exposed to such comments. Consequently, not only does frequent exposure to HS influence an individual's beliefs as observed in earlier studies, but it also impacts psychophysiological reactions to derogatory language.

Validation of the multidimensional bystander responses to racist hate speech scale and its association with empathy and moral disengagement among adolescents.

Our understanding of how bystanders respond to hate speech is limited. This may be due, in part, to the lack of available measurement tools. However, understanding adolescents' responses to hate speech is critical because this kind of research can support schools in empowering students to exhibit courageous moral behavior. Thus, the purpose of the present study was to investigate the psychometric properties of the newly developed Multidimensional Bystander Responses to Hate Speech Scale (MBRHS) and to explore demographic differences and correlates of bystander behavior in school hate speech. The sample consisted of 3225 seventh to ninth graders from Germany and Switzerland. Exploratory and confirmatory factor analyses supported a model with seven factors. We found that adolescents with immigrant background and boys showed particularly unfavorable response patterns. In addition, our study suggests that empathy is positively correlated with the factors comforting the victim, seeking help at school, and countering hate speech but negatively correlated with helplessness, revenge, reinforcing, and ignoring. Moral disengagement showed the opposite correlational pattern. The findings indicate that the MBRHS is a psychometrically valid and reliable measure that could aid in measuring varied responses to hate speech. In addition, this work highlights the relevance of empathy and moral engagement training in anti-hate speech prevention programs.

Direct and indirect effects of social dominance orientation on hate speech perpetration via empathy and moral disengagement among adolescents: A multilevel mediation model.

Although it is known that social dominance orientation directly affects hate speech perpetration, few studies have explored the mechanisms by which this effect takes place during adolescence. Based on the socio-cognitive theory of moral agency, we aimed to fill this gap in the literature by exploring the direct and indirect effects of social dominance orientation on hate speech perpetration in offline and online settings. The sample included seventh, eigth, and ninth graders (N = 3225) (51.2% girls, 37.2% with an immigrant background) from 36 Swiss and German schools who completed a survey about hate speech, social dominance orientation, empathy, and moral disengagement. A multilevel mediation path model revealed that social dominance orientation had a direct effect on offline and online hate speech perpetration. Moreover, social dominance also had indirect effects via low levels of empathy and high levels of moral disengagement. No gender differences were observed. Our findings are discussed regarding the potential contribution to preventing hate speech during adolescence.

Hate Speech Against Asian American Youth: Pre-Pandemic Trends and The Role of School Factors.

Although hate speech against Asian American youth has intensified in recent years-fueled, in part, by anti-Asian rhetoric associated with the COVID-19 pandemic-the phenomenon remains largely understudied at scale and in relation to the role of schools prior to the pandemic. This study describes the prevalence of hate speech against Asian American adolescents in the US between 2015 and 2019 and investigates how school-related factors are associated with whether Asian American youth are victims of hate speech at school. Analyses are based on a sample of 938 Asian American adolescents (Mage = 14.8; 48% female) from the three most recently available waves (2015, 2017, and 2019) of the School Crime Supplement to the National Crime Victimization Survey. On average, approximately 7% of Asian Americans were targets of hate speech at school between 2015 and 2019, with rates remaining stable over time. Findings also indicate that students had lower odds of experiencing hate speech if they attended schools with a stronger authoritative school climate, which is characterized by strict, yet fair disciplinary rules coupled with high levels of support from adults. On the other hand, Asian American youth faced higher odds of experiencing hate speech if they were involved in school fights. Authoritative school climate and exposure to fights are malleable and can be shaped directly by broader school climate related policies, programs and interventions. Accordingly, efforts to promote stronger authoritative climates and reduce exposure to physical fights hold considerable potential in protecting Asian American youth from hate speech at school.

Shockwavelike Behavior across Social Media.

Online communities featuring "anti-X" hate and extremism, somehow thrive online despite moderator pressure. We present a first-principles theory of their dynamics, which accounts for the fact that the online population comprises diverse individuals and evolves in time. The resulting equation represents a novel generalization of nonlinear fluid physics and explains the observed behavior across scales. Its shockwavelike solutions explain how, why, and when such activity rises from "out-of-nowhere," and show how it can be delayed, reshaped, and even prevented by adjusting the online collective chemistry. This theory and findings should also be applicable to anti-X activity in next-generation ecosystems featuring blockchain platforms and Metaverses.

Biased hate crime perceptions can reveal supremacist sympathies.

People may be sympathetic to violent extremism when it serves their own interests. Such support may manifest itself via biased recognition of hate crimes. Psychological surveys were conducted in the wakes of mass shootings in the United States, New Zealand, and the Netherlands (total n = 2,332), to test whether factors that typically predict endorsement of violent extremism also predict biased hate crime perceptions. Path analyses indicated a consistent pattern of motivated judgment: hate crime perceptions were directly biased by prejudicial attitudes and indirectly biased by an aggrieved sense of disempowerment and White/Christian nationalism. After the shooting at a synagogue in Pittsburgh, Pennsylvania, disempowerment-fueled anti-Semitism predicted lower perceptions that the gunman was motivated by hatred and prejudice (study 1). After the shootings that occurred at two mosques in Christchurch, New Zealand, disempowerment-fueled Islamoprejudice similarly predicted lower hate crime perceptions (study 2a). Conversely, after the tram shooting in Utrecht, Netherlands (which was perpetrated by a Turkish-born immigrant), disempowerment-fueled Islamoprejudice predicted higher hate crime perceptions (study 2b). Finally, after the Walmart shooting in El Paso, Texas, hate crime perceptions were specifically biased by an ethnonationalist view of Hispanic immigrants as a symbolic (rather than realistic) threat to America; that is, disempowered individuals deemphasized likely hate crimes due to symbolic concerns about cultural supremacy rather than material concerns about jobs or crime (study 3). Altogether, biased hate crime perceptions can be purposive and reveal supremacist sympathies.

Restorative solutions for anti-LGBT victimisation experiences: potential pathways for victims' wellbeing and key challenges and needs.

The victims of anti-LGBT hate crimes may have particularly negative experiences which affect their mental health and wellbeing. These incidents affect the victims' self-esteem, dignity and identity, and they also affect indirect victims in similar ways. As opposed to retributive justice, restorative justice may offer a more satisfactory justice experience for those affected, by addressing the harm caused to them. This is due to the fact that restorative processes require flexibility, adequacy and tailor-made design. Drawing on findings from a multi-site qualitative study conducted in six European countries, this article discusses the perceptions and experiences of key professionals regarding the potential of restorative justice to provide for victims of anti-LGBT hate crimes, particularly in relation to repairing the individual and collective harm caused by such crimes.

The relation of classroom climate to adolescents' countering hate speech via social skills: A positive youth development perspective.

INTRODUCTION: Hate speech is a current challenge for schools around the globe. At the same time, students worldwide stand up to hate speech by countering it. Guided by a positive youth development perspective, the present study investigated the direct and indirect associations between classroom climate (environmental assets), social skills (personal assets), and countering hate speech (as a proxy of thriving) among adolescents. METHODS: The sample included 3225 students in grades 7-9 (51.7% self-identified as female) from 40 schools in Germany (n = 1841) and Switzerland (n = 1384). Students completed self-report questionnaires that assessed classroom climate, three facets of social skills (i.e., perspective-taking, prosocial behavior, assertiveness), and counterspeech. RESULTS: The results of the 2-(1-1-1)-1 multilevel mediation analysis revealed that classroom climate (L2) and the three facets of social skills (L1) had a direct positive effect on counterspeech (L1). Furthermore, classroom climate (L2) also had a direct positive effect on the three facets of social skills (L1). Finally, classroom climate (L2) had an indirect positive effect on counterspeech (L1) via all three aspects of social skills (L1). CONCLUSION: The findings highlight that successful anti-hate speech programs may entail a combination of environmental and personal factors for increasing adolescents' active contribution to an inclusive and discrimination-free classroom environment where hate speech is not tolerated.

Resisting and countering online racial hate: Antiracism advocacy and coping online with racism as moderators of distress associated with online racism.

The emerging literature highlights online racism (e.g., racist online interactions, exposure to racially traumatizing content) as a contemporary stressor among racially/ethnically minoritized adults. Thus, identifying factors that can help buffer the harmful impact of online racism are imperative. We examined engagement in antiracism advocacy and online coping as moderators that can potentially buffer the link between online racism and psychological distress. Using data from 395 racially/ethnically diverse adults (Mage = 34.12, SD = 11.19), we conducted latent moderated structural equation modeling to test individual and institutional antiracism advocacy, and online coping as moderators in the link between online racism and psychological distress. Individual antiracism advocacy was not a significant moderator. Both institutional antiracism advocacy and online coping were significant moderators. For both variables, the link between online racism and psychological distress was not significant at 1 SD below the mean but was significant at the mean and 1 SD above the mean. Thus, distress associated with online racism was not significant among those engaging in low levels of institutional antiracism advocacy and online coping but mean to high levels of institutional antiracism advocacy and online coping did not appear to be protective against distress. Implications for advancing the research on coping with online racism are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Roman Urdu Hate Speech Detection Using Transformer-Based Model for Cyber Security Applications.

Social media applications, such as Twitter and Facebook, allow users to communicate and share their thoughts, status updates, opinions, photographs, and videos around the globe. Unfortunately, some people utilize these platforms to disseminate hate speech and abusive language. The growth of hate speech may result in hate crimes, cyber violence, and substantial harm to cyberspace, physical security, and social safety. As a result, hate speech detection is a critical issue for both cyberspace and physical society, necessitating the development of a robust application capable of detecting and combating it in real-time. Hate speech detection is a context-dependent problem that requires context-aware mechanisms for resolution. In this study, we employed a transformer-based model for Roman Urdu hate speech classification due to its ability to capture the text context. In addition, we developed the first Roman Urdu pre-trained BERT model, which we named BERT-RU. For this purpose, we exploited the capabilities of BERT by training it from scratch on the largest Roman Urdu dataset consisting of 173,714 text messages. Traditional and deep learning models were used as baseline models, including LSTM, BiLSTM, BiLSTM + Attention Layer, and CNN. We also investigated the concept of transfer learning by using pre-trained BERT embeddings in conjunction with deep learning models. The performance of each model was evaluated in terms of accuracy, precision, recall, and F-measure. The generalization of each model was evaluated on a cross-domain dataset. The experimental results revealed that the transformer-based model, when directly applied to the classification task of the Roman Urdu hate speech, outperformed traditional machine learning, deep learning models, and pre-trained transformer-based models in terms of accuracy, precision, recall, and F-measure, with scores of 96.70%, 97.25%, 96.74%, and 97.89%, respectively. In addition, the transformer-based model exhibited superior generalization on a cross-domain dataset.

Qualitative and Artificial Intelligence-based Sentiment Analyses of Anti-LGBTI+ Hate Speech on Twitter in Turkey.

The aim of this study was to evaluate hate speech in Turkish LGBTI+-related tweets during a one-month period of artificial intelligence-based sentiment analyses. Turkish tweets related to LGBTI+, were retrieved using Python library Tweepy and were evaluated by sentiment analysis. The researchers then performed a qualitative analysis of the most frequently liked and retweeted tweets (n = 556). Sentiment analysis revealed that 69.5% of tweets were negative, 23.3% were neutral, and 7.2% were positive. The qualitative analysis was grouped under seven themes: LGBTI+ Club; Terrorism and Terrorist Organization Membership; Perversion, Illness, Immorality; Presence in History; Religious References; Insults; and Humiliation. The results of this study show that anti-LGBTI+ hate speech in Turkey is significant in terms of both quality and quantity. As LGBTI+ individuals are at risk for excess mental distress and disorders, it is important to understand the risks and other factors that ameliorate stress and contribute to mental health in social media.