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.

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.

Automatic Identification of Hate Speech - A Case-Study of alt-Right YouTube Videos.

Background: Identifying hate speech (HS) is a central concern within online contexts. Current methods are insufficient for efficient preemptive HS identification. In this study, we present the results of an analysis of automatic HS identification applied to popular alt-right YouTube videos. Methods: This essay describes methodological challenges of automatic HS detection. The case study concerns data on a formative segment of contemporary radical right discourse. Our purpose is twofold. (1) To outline an interdisciplinary mixed-methods approach for using automated identification of HS. This bridges the gap between technical research on the one hand (such as machine learning, deep learning, and natural language processing, NLP) and traditional empirical research on the other. Regarding alt-right discourse and HS, we ask: (2) What are the challenges in identifying HS in popular alt-right YouTube videos? Results: The results indicate that effective and consistent identification of HS communication necessitates qualitative interventions to avoid arbitrary or misleading applications. Binary approaches of hate/non-hate speech tend to force the rationale for designating content as HS. A context-sensitive qualitative approach can remedy this by bringing into focus the indirect character of these communications. The results should interest researchers within social sciences and the humanities adopting automatic sentiment analysis and for those analysing HS and radical right discourse. Conclusions: Automatic identification or moderation of HS cannot account for an evolving context of indirect signification. This study exemplifies a process whereby automatic hate speech identification could be utilised effectively. Several methodological steps are needed for a useful outcome, with both technical quantitative processing and qualitative analysis being vital to achieve meaningful results. With regard to the alt-right YouTube material, the main challenge is indirect framing. Identification demands orientation in the broader discursive context and the adaptation towards indirect expressions renders moderation and suppression ethically and legally precarious.

Do All Victimization Types Cluster? Assessing the Co-Occurrence of Hate Speech With Criminal and Bullying Victimization at School.

Evidence indicates that poly-victimization relates to greater victim impacts than single-type victimization. A separate body of research finds that victimization motivated by bias is associated with elevated harm. However, little empirical work has assessed whether youth who experience biased victimization are poly-victims, and studies have not sufficiently examined the potentially deleterious effects of experiencing both types of victimization. This study uses nationally representative data to examine the prevalence of school-based crime and bullying victimization among hate speech victims. I then assess the odds of experiencing fear and avoiding places at school across victimization types. The findings reveal that a considerable number of hate speech victims are also bullied and that experiencing any two types of victimization is associated with increased odds of fear and avoidance. These results highlight the importance of targeting biased victimization in anti-bullying programming.

Beyond the Binary: The Issue of Intra-Minority Hostility and the Need to Challenge Conventional Victim/Perpetrator Frameworks Within Hate Studies.

It is well-established within the hate studies literature that the majority of hate crimes and incidents of targeted hostility are perpetrated by those in the "majority" society. In the UK, academic and official research consistently shows that young White, British males are most commonly the culprits of all forms of targeted victimization, especially racist hate. However, urban areas of "super-diversity" offer researchers an opportunity to understand hate crime victimization and perpetration in a more nuanced and comprehensive way. Hate studies research has slowly begun to highlight instances of people from marginalized and stigmatized groups being targeted on the basis of their identity by individuals who are also members of minority groups, sometimes even the same minority group as the victim. Very little is understood about this particular victimizing dynamic other than it appears to be an attempt by minority group members to "fit in" by adopting what they perceive to be majority group values and attitudes. By drawing from 44 qualitative in-depth interviews exploring the experiences of new migrants and refugees and observations from 20 months of grassroots engagement, this article challenges established theories of "othering" that overwhelmingly refer to binary, static majority/minority tensions. The stories of these too-often "hidden" victims of targeted hostility offer a fresh perspective on the relationships between victims of hate and perpetrators. The article also contributes new explanations as to why those who are often targeted go on to target others.

Punishing "Privilege": Antisemitic Hate Crime in Canada.

Both federal government and civil society organization data point to consistently rising incidents of antisemitic narratives and acts across Canada. In spite of this, antisemitic hate crime has not been the focus of any academic research here, some would argue because Jews are not typically thought to be an at-risk community. Rather, the Jewish community is thought to occupy a relatively privileged place in society which shields them from bias motivated attacks. Countering this narrative, our study, based in Ontario and Quebec, reveals that Jewish individuals and institutions are highly vulnerable to discursive, physical, and property violations. Many of those we spoke with felt embattled by the narrative attacks that rendered the community vulnerable to corollary physical attacks. Of particular significance are the enabling images of Jews that equate "Jewish privilege" with excessive power and control. We explore these themes, concluding with calls for strategies intended to counter hateful narratives.

Hidden Hate, Hidden Violence: Dismantling Myths and Identifying Fresh Challenges for Research and Policy.

Hate crime has become an increasingly familiar term within global scholarship, with advancements in conceptual understanding and empirical knowledge helping to generate improved policy responses across many parts of the world. However, the continued demonization of 'other' identities, the escalating volume of hate incidents worldwide and the prevailing climate of rising tensions, decreasing resources and political de-prioritization all suggest that many urgent challenges remain. Contributors to this special issue have dismantled common stereotypes and misperceptions which hamper our collective capacity to address contemporary expressions of hate and violence. In doing so, they draw from their research evidence to identify "hidden" challenges which should be at the forefront of attempts to address the causes, effects, and prevention of all forms of violence. This call for reconfiguration is the unifying theme which runs through each article, and which paves the way for more nuanced analyses that offer new frameworks for responding to the diverse and changing patterns of violence. These are challenges which straddle disciplinary boundaries, geographical borders, and the physical/digital world, and which demand the international, intersectional, and interdisciplinary perspectives evident within this special issue.

The Ambivalence of Far-Right Women: Hate, Trauma, Gender, and Neoliberalism in Contemporary Japan.

While far-right movements are commonly associated with masculinity and women are in the minority, it is notable that they often play significant roles within these movements. To deepen our understanding of the motivations behind women's participation, this study challenges Blee's argument that women's motivations for participating are shaped by their interactions with other members. By using the psychosocial method devised by Hollway and Jefferson and developed by Gadd, the present study argues that women's pre-participation experiences can play a vital part in drawing them to the movements. Through analyzing the life stories of six far-right women in Japan and conducting an in-depth case study of three of them, the study aims to uncover a wide range of experiences that may initially appear unrelated to far-right ideology but ultimately led these subjects to become involved in far-right movements. It highlights the importance of paying attention to their complex subjectivities, which are formed by the interplay between their unique trajectories and societal transitions concerning gender norms, particularly within the era of neoliberal "emancipation." The study finds that the duality of far-right movements, which combine conservatism with deviance, enables some women to express paradoxical desires that they experience in response to living through a transitional era.

Working "With" Not "On" Disabled People: The Role of Hate Crime Research within the Community.

For many disabled people, violence can become an unwanted, yet ordinary part of everyday life. Often, these crimes are attributed to understandings of disabled people as vulnerable and largely, passive victims. Attending to the aims of this special issue, this paper aims to dismantle these stereotypes and attend to the unique ways that disabled people can resist and respond to hate crime through creative and collaborative research practices. Building upon this, I argue that there is a pressing need for hate studies researchers to work "with" and not "on" those who have experienced targeted violence. Working in this way builds upon long-standing efforts of disabled activists and disabilities studies researchers to challenge reductive research practices by working in more collective and inclusive ways. To demonstrate this, I reflect upon a project working in partnership with disabled people to create a disability hate crime toolkit. The toolkit, now published, shares accessible and informative resources that can be used to raise awareness about disability hate crime. While the focus of this paper is disability, I consider methods of collaboration, co-production and participation that can be drawn upon by researchers to respond to hate crime and interpersonal violence more broadly.

Lost in Translation? Applying the Hate Crime Concept to an Indian Context.

Hate crime is increasingly a familiar term within the domains of scholarship, policy, and activism as the harms associated with acts of targeted hostility continue to pose complex, global challenges. However, an exclusively Western-centric focus has done little to foster transnational conversations or to shape conceptual or legal frameworks in parts of the world where the challenges posed by hate and prejudice remain underexplored despite their devastating consequences. This article considers how the complexities and specificities of the Indian context disrupt the dominant assumptions of conventional hate crime frameworks. In doing so, it highlights the value of extending conventional Westernized models of thinking to different environments with different sets of challenges. Through its analysis of caste crimes and the factors that reinforce a prevailing institutional and cultural backdrop of political indifference, bureaucratic resistance, and public skepticism, the article illustrates why and how key elements of the Western framework remain ill-suited to the Indian context. The authors call instead for a creative translation of the hate crime concept, which accommodates the nature of violence within specific social contexts, and which emphasizes the institutional features that can mitigate the limitations of state capacity and intent. The process of translation has value in harnessing the benefits of the hate crime concept within countries, which lack a common framework to foster shared understanding and prioritization in relation to tackling contemporary expressions of hate. At the same time, this process enriches prevailing thinking, dismantles stereotypes, and challenges scholars of targeted violence to familiarize themselves with the unfamiliar.

Hating Women: A Constitution of Hate in Plain Sight.

In April 2023, the U.K. government announced that misogyny would not be categorized as a hate crime stating that this "may prove more harmful than helpful." This article argues that before and beyond hate crime, misogyny, understood as the hatred of women (from the Greek misein [hate] gynae [women]), is the foundational logic of our legal, social, and political order in the west. This constitution of hate relies on the active dehumanization, exploitation, and ownership of women's bodies by the institution of white men through making women the object of the "colonization of the everyday." This exhausting hatred is enacted through repetitive, unceasing, and everyday violence toward women. Simply put, patriarchal, colonial, capitalist democracy is only sustained through violence against women. Hating women is, therefore, not a pathology of society but rather is the necessary existence condition of our legal-political constitution, clear to see yet hiding in plain sight. Misogyny ensures the precarity of women's bodies and women's status as trespassers in everyday spaces that are deliberately always already misogynistic. Given the foundational nature of misogyny, did the government have a point in excluding endemic violence against women from hate crime as "more harmful than helpful?" Is hate crime merely constitutive of a cultural matrix of misogyny? This paper enacts a decolonial feminist prism to disrupt the cultural condition of misogyny by thinking hate crime together with legal-political constitutional and cultural change. The paper explores violence against women set against the historical emergence of misogyny from Greek dehumanization, to medieval persecution of "witches," the muzzling and banning of women from public spaces, Shakespeare's "Taming," to contemporary femicide rates. Interrogating hate crime through this prism offers nuanced routes for how to disrupt the legal-political constitution of misogyny that is neither hidden nor new. Misogyny is enduring.

In-party love spreads more efficiently than out-party hate in online communities.

In this article, we present the findings of a comprehensive longitudinal social network analysis conducted on Twitter across four consecutive election campaigns in Spain, spanning from 2015 to 2019. Our focus is on the discernible trend of increasing partisan and ideological homogeneity within interpersonal exchanges on this social media platform, alongside high levels of networking efficiency measured through average retweeting. This diachronic study allows us to observe how dynamics of party competition might contribute to perpetuating and strengthening network ideological and partisan homophily, creating 'epistemic bubbles' in Twitter, yet showing a greater resistance to transforming them into 'partisan echo-chambers.' Specifically, our analysis reveals that the rise of a new radical right-wing party (RRP), Vox, has heightened ideological homogeneity among users across the entire ideological spectrum. However, this process has not been uniform. While users aligned with mainstream political parties consistently share content that reinforces in-party affinity, resulting in highly efficient 'epistemic bubbles,' the emergence of the RRP has given rise to a distinct group of users associated with the most extreme partisan positions, characterized by a notable proportion of out-partisan hostility content, which has fostered the creation of low-efficient 'partisan echo-chambers.'

Cross-lingual hate speech detection using domain-specific word embeddings.

THIS ARTICLE USES WORDS OR LANGUAGE THAT IS CONSIDERED PROFANE, VULGAR, OR OFFENSIVE BY SOME READERS. Hate speech detection in online social networks is a multidimensional problem, dependent on language and cultural factors. Most supervised learning resources for this task, such as labeled datasets and Natural Language Processing (NLP) tools, have been specifically tailored for English. However, a large portion of web users around the world speak different languages, creating an important need for efficient multilingual hate speech detection approaches. In particular, such approaches should be able to leverage the limited cross-lingual resources currently existing in their learning process. The cross-lingual transfer in this task has been difficult to achieve successfully. Therefore, we propose a simple yet effective method to approach this problem. To our knowledge, ours is the first attempt to create a multilingual embedding model specific to this problem. We validate the effectiveness of our approach by performing an extensive comparative evaluation against several well-known general-purpose language models that, unlike ours, have been trained on massive amounts of data. We focus on a zero-shot cross-lingual evaluation scenario in which we classify hate speech in one language without having access to any labeled data. Despite its simplicity, our embeddings outperform more complex models for most experimental settings we tested. In addition, we provide further evidence of the effectiveness of our approach through an ad hoc qualitative exploratory analysis, which captures how hate speech is displayed in different languages. This analysis allows us to find new cross-lingual relations between words in the hate-speech domain. Overall, our findings indicate common patterns in how hate speech is expressed across languages and that our proposed model can capture such relationships significantly.

Examining mental health correlates of hate-motivated behaviour in Scotland: An investigation of victims, perpetrators and victim-perpetrators.

BACKGROUND: Hate-motivated behaviour (HMB) ranges from microaggressions to criminal acts and is a public health concern with wide-ranging consequences. AIMS: The current study aimed to examine the mental health correlates of HMB perpetration, victimisation and co-occurring victimisation/perpetration. METHODS: Participants (n = 447) completed an online cross-sectional survey assessing demographic factors, HMB (perpetration and victimisation), positive mental wellbeing and symptoms of depression and anxiety. RESULTS: HMB victimisation was associated with lower positive mental wellbeing and increased symptoms of anxiety and depression. However, neither HMB perpetration nor co-occurring perpetration/victimisation were associated with any of the three mental health outcome measures. CONCLUSION: Experiencing HMB as a victim is linked to increased psychological distress. Additional research, which focuses on sampling populations who are known to be at greater risk for involvement in HMB, is needed to fully understand the impact of the victim-offender overlap on mental health outcomes.

Anti-Asian Hate and the Health of Older Asian Individuals.

This qualitative study assesses the association of anti-Asian hate with older Asian individuals' health and the clinician's role in addressing hate incidents.

"Hate has chipped away at my sense of security": A survey study of the impacts of anti-Asian hate on the health of older Asians.

BACKGROUND: Since the beginning of the COVID-19 pandemic, older Asians have experienced a rise in racism and discrimination based on their race and ethnicity. This study examines how anti-Asian hate impacts older Asians' mental, social, and physical health. METHODS: From March 18, 2022 to January 24, 2023, we conducted a cross-sectional survey study of community-dwelling Asian/Asian American adults aged ≥50 years from the San Francisco Bay Area. Measures included perceptions of anti-Asian hate; direct encounters with hate incidents; indirect experiences with hate incidents (e.g. knowing a friend who was a victim); reports of anxiety, depression, loneliness, and changes in daily activities; ways to address these issues; and discussions with clinicians about hate incidents. RESULTS: Of the 293 older Asians, 158 (54%) were Vietnamese and 97 (33%) Chinese. Eighty-five (29%) participants were direct victims of anti-Asian hate, 112 (38%) reported anxiety, 105 (36%) reported depression, 161 (55%) reported loneliness, and 142 (48%) reported decreased daily activities. Compared with those who were "not-at-all" to "moderately" worried about hate incidents, participants who were "very" to "extremely" worried experienced heightened anxiety (42% versus 16%), loneliness (30% versus 14%), and changes in daily activities (66% versus 31%), p < 0.01 for all. Most participants (72%) felt comfortable discussing hate incidents with clinicians; however, only 31 (11%) reported that a clinician had talked with them about these incidents. CONCLUSION: Both directly and indirectly, anti-Asian hate negatively impacts older Asians' mental, social, and physical health. Clinicians have a role in addressing the health impacts of anti-Asian hate.

The anatomy of prejudice during pandemic lockdowns: Evidence from a national panel study.

During the early stages of the COVID-19 pandemic, there was a spike in the reporting of hate crimes (Human Rights Watch, 2020). However, the extent to which the pandemic affected prejudice across a general population-not merely among those disposed to hate crimes-remains unclear. Also unclear is the extent to which prejudice was restricted to specific minority groups associated with the virus, or whether prejudice spilled over to other minority groups. To address these questions, we use panel data collected from participants in a large national longitudinal (panel) study of New Zealanders before and during the early COVID-19 pandemic and systematically quantified social warmth ratings across a broad range of minority-groups (The New Zealand Attitudes and Values Study, N = 30,327, years 2018-2020). We discover reduced warmth toward Chinese, Asians (broadly defined), immigrants, Muslims, refugees, Indians, and the mentally ill. In absolute terms, warmth towards Chinese decreased the most (0.11 SD). Notably, changes in warmth were not detected toward NZ Europeans, Maori, Pacific Islanders, the overweight, or the elderly. Overall, these findings suggest that in New Zealand, pandemic prejudice may spread beyond minority groups associated with the virus to other groups perceived as non-prototypical of national identity.

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.

Love and hate do not modulate the attentional blink but improve overall performance.

How may feelings of love and hate impact people's attention? We used a modified Attentional Blink (AB) task in which 300 participants were asked to categorise a name representing a person towards whom they felt either hate, love, or neutral (first target) plus identify a number word (second target), both embedded in a rapidly presented stream of other words. The lag to the second target was systematically varied. Contrary to our hypothesis, results revealed that both hated and loved names resulted in higher accuracy for the second target than neutral names, which was largely independent of lag. Also, there we observed no sustained transfer effects of love and hate onto neutral name trials. The findings differ from prior research on attentional blink and transient, non-personal, stimulus-driven emotions, suggesting that interpersonal feelings activate different attention-relevant mechanisms. Relevant to future research, we speculate that love and hate are motivators of goal-directed behaviour that facilitate subsequent information processing.