Hate, crime and epistemic vulnerability: on sense-making and feelings of (un)safety among Danish Muslims.

This article investigates feelings of (un)safety emerging from knowing and sharing knowledge about hate crime and hate incidents. Drawing on fieldwork and interviews with young Muslims living in the greater Copenhagen area, the article explores the way the interlocutors seek to make sense of their experiences through available epistemic categories, and how this sense-making is shaped by reactions from the surrounding society, e.g., whether it is questioned, supported, ignored etc. Combining criminological and psychological research on direct and indirect harms of hate crime with insights from philosophy on epistemic encounters and their ethical implications the article provides a framework for investigating safety in epistemic interactions. Based on this framework, the article show the often hard work that people perform in order to balance epistemic needs (e.g. the need for knowledge and for recognition) with epistemic risks (e.g. the risk of testimonial rejection, of damaged epistemic confidence, or loss of credibility).

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.

Preventing Religion-Based Hate Crime Victimization Among Youth: A Systematic Review of Personal, Collective, and Policy Responses.

Hate crime victimization targeting the victim's religious identity poses a serious problem for individuals, communities, and societies. This systematic review describes countermeasures to such victimization, aiming for broad descriptive inclusion by canvassing personal adaptations, collective programs, and institutional-governmental policies. Targeting peer-reviewed articles published between 2002 and 2022, we found 44 articles describing measures related to religion-based victimization prevention. We classified the studied measures into 12 main types. The most salient personal adaptations included camouflage-type blending in to avoid victimization, using religion as a source of resilience, and changing routines to deflect risk. At the collective level, mobilizing community resilience, stereotype reduction, and place-based solutions were often researched. The relatively few institutional-level studies addressed measures to enhance the connection between victims and authorities by various means. The experimental studies heavily concentrated on experiments supporting the efficacy of changing people's perceptions as a means of prevention. The review concludes with a discussion about research and policy implications.

How does the media contribute to the rise of hate crimes against foreign domestic helpers in Hong Kong? An unfair problem frame and agenda setting.

Hate crimes are widespread in Hong Kong society. Foreign domestic helpers working in Hong Kong also experience unfair agenda-setting by the media due to their dual economic and social disadvantages, and the media tries to portray them in a hostile social role. At the same time, the media creates negative social images of minority groups through news coverage, which leads to an increase in social hate crimes against them. This study used WiseSearch, a Chinese newspaper collection and analysis platform, to explore how Hong Kong news media use news themes and content to create a negative image of Hong Kong foreign domestic helpers in order to understand the media origins of hate crimes against Hong Kong foreign domestic helpers. Ultimately, the study found that local news media in Hong Kong are more inclined to cover the legal disputes of foreign domestic helpers in the agenda-setting process. In addition, they are more likely to associate foreign domestic helpers with "fear" rather than "rest assured." The study also found that because of the news value orientation, Hong Kong media tended to treat foreign domestic helpers as outsiders and less sympathetically when writing news stories.

Hate speech detection with ADHAR: a multi-dialectal hate speech corpus in Arabic.

Hate speech detection in Arabic poses a complex challenge due to the dialectal diversity across the Arab world. Most existing hate speech datasets for Arabic cover only one dialect or one hate speech category. They also lack balance across dialects, topics, and hate/non-hate classes. In this paper, we address this gap by presenting ADHAR-a comprehensive multi-dialect, multi-category hate speech corpus for Arabic. ADHAR contains 70,369 words and spans four language variants: Modern Standard Arabic (MSA), Egyptian, Levantine, Gulf and Maghrebi. It covers four key hate speech categories: nationality, religion, ethnicity, and race. A major contribution is that ADHAR is carefully curated to maintain balance across dialects, categories, and hate/non-hate classes to enable unbiased dataset evaluation. We describe the systematic data collection methodology, followed by a rigorous annotation process involving multiple annotators per dialect. Extensive qualitative and quantitative analyses demonstrate the quality and usefulness of ADHAR. Our experiments with various classical and deep learning models demonstrate that our dataset enables the development of robust hate speech classifiers for Arabic, achieving accuracy and F1-scores of up to 90% for hate speech detection and up to 92% for category detection. When trained with Arabert, we achieved an accuracy and F1-score of 94% for hate speech detection, as well as 95% for the category detection.

"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.

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.

A survey on multi-lingual offensive language detection.

The prevalence of offensive content on online communication and social media platforms is growing more and more common, which makes its detection difficult, especially in multilingual settings. The term "Offensive Language" encompasses a wide range of expressions, including various forms of hate speech and aggressive content. Therefore, exploring multilingual offensive content, that goes beyond a single language, focus and represents more linguistic diversities and cultural factors. By exploring multilingual offensive content, we can broaden our understanding and effectively combat the widespread global impact of offensive language. This survey examines the existing state of multilingual offensive language detection, including a comprehensive analysis on previous multilingual approaches, and existing datasets, as well as provides resources in the field. We also explore the related community challenges on this task, which include technical, cultural, and linguistic ones, as well as their limitations. Furthermore, in this survey we propose several potential future directions toward more efficient solutions for multilingual offensive language detection, enabling safer digital communication environment worldwide.

A systematic literature review of hate speech identification on Arabic Twitter data: research challenges and future directions.

The automatic speech identification in Arabic tweets has generated substantial attention among academics in the fields of text mining and natural language processing (NLP). The quantity of studies done on this subject has experienced significant growth. This study aims to provide an overview of this field by conducting a systematic review of literature that focuses on automatic hate speech identification, particularly in the Arabic language. The goal is to examine the research trends in Arabic hate speech identification and offer guidance to researchers by highlighting the most significant studies published between 2018 and 2023. This systematic study addresses five specific research questions concerning the types of the Arabic language used, hate speech categories, classification techniques, feature engineering techniques, performance metrics, validation methods, existing challenges faced by researchers, and potential future research directions. Through a comprehensive search across nine academic databases, 24 studies that met the predefined inclusion criteria and quality assessment were identified. The review findings revealed the existence of many Arabic linguistic varieties used in hate speech on Twitter, with modern standard Arabic (MSA) being the most prominent. In identification techniques, machine learning categories are the most used technique for Arabic hate speech identification. The result also shows different feature engineering techniques used and indicates that N-gram and CBOW are the most used techniques. F1-score, precision, recall, and accuracy were also identified as the most used performance metric. The review also shows that the most used validation method is the train/test split method. Therefore, the findings of this study can serve as valuable guidance for researchers in enhancing the efficacy of their models in future investigations. Besides, algorithm development, policy rule regulation, community management, and legal and ethical consideration are other real-world applications that can be reaped from this research.

Multilingual Hate Speech Detection: A Semi-Supervised Generative Adversarial Approach.

Social media platforms have surpassed cultural and linguistic boundaries, thus enabling online communication worldwide. However, the expanded use of various languages has intensified the challenge of online detection of hate speech content. Despite the release of multiple Natural Language Processing (NLP) solutions implementing cutting-edge machine learning techniques, the scarcity of data, especially labeled data, remains a considerable obstacle, which further requires the use of semisupervised approaches along with Generative Artificial Intelligence (Generative AI) techniques. This paper introduces an innovative approach, a multilingual semisupervised model combining Generative Adversarial Networks (GANs) and Pretrained Language Models (PLMs), more precisely mBERT and XLM-RoBERTa. Our approach proves its effectiveness in the detection of hate speech and offensive language in Indo-European languages (in English, German, and Hindi) when employing only 20% annotated data from the HASOC2019 dataset, thereby presenting significantly high performances in each of multilingual, zero-shot crosslingual, and monolingual training scenarios. Our study provides a robust mBERT-based semisupervised GAN model (SS-GAN-mBERT) that outperformed the XLM-RoBERTa-based model (SS-GAN-XLM) and reached an average F1 score boost of 9.23% and an accuracy increase of 5.75% over the baseline semisupervised mBERT model.

Mapping the scientific knowledge and approaches to defining and measuring hate crime, hate speech, and hate incidents: A systematic review.

Background: The difficulties in defining hate crime, hate incidents and hate speech, and in finding a common conceptual basis constitute a key barrier toward operationalisation in research, policy and programming. Definitions disagree about issues such as the identities that should be protected, the types of behaviours that should be referred to as hateful, and how the 'hate element' should be assessed. The lack of solid conceptual foundations is reflected in the absence of sound data. These issues have been raised since the early 1990s (Berk, 1990; Byers & Venturelli, 1994) but they proved to be an intractable problem that continues to affect this research and policy domain. Objectives: Our systematic review has two objectives that are fundamentally connected: mapping (1) original definitions and (2) original measurement tools of hate crime, hate speech, hate incidents and surrogate terms, that is, alternative terms used for these concepts (e.g., prejudice-motivated crime, bias crime, among many others). Search Methods: We systematically searched over 19 databases to retrieve academic and grey literature, as well as legislation. In addition, we contacted 26 country experts and searched 211 websites, as well as bibliographies of published reviews of related literature, and scrutiny of annotated bibliographies of related literature. Inclusion Criteria: This review included documents published after 1990 found in academic literature, grey literature and legislation. We included academic empirical articles with any study design, as well as theoretical articles that focused specifically on defining hate crime, hate speech, hate incidents or surrogate terms. We also reviewed current criminal or civil legislation that is intended to regulate forms of hate speech, hate incidents and hate crimes. Eligible countries included Canada, USA, UK, Ireland, Germany, France, Italy, Spain, Australia and New Zealand. For documents to be included in relation to research objective (1), they had to contain at least one original definition of hate speech, hate incidents or hate crimes, or any surrogate term. For documents to be included in relation to research objective (2), they had to contain at least one original measurement tool of hate speech, hate incidents or hate crimes, or any surrogate term. Documents could be included in relation to both research objectives. Data Collection and Analysis: The systematic search covered 1 January 1990 to 31 December 2021, with searches of academic databases conducted between 8th March and 12th April 2022 yielding 35,191 references. We carried out country-specific searches for grey literature published in the same time period between 27th August and 2nd December 2021. These searches yielded a total of 2748 results. We coded characteristics of the definitions and measurement tools, including the protected characteristics, the approaches to categorise the 'hate element' and other variables. We used univariate and bivariate statistical methods for data analysis. We also carried out a social network analysis. Main Results: We provide as annex complete lists of the original definitions and measurement tools that met our inclusion criteria, for the use of researchers and policy makers worldwide. We included 423 definitions and 168 measurement tools in academic and grey literature, and 83 definitions found in legislation. To support future research and policy work in this area, we included a synthetic assessment of the (1) the operationalisability of each definition and (2) the theoretical robustness and transparency of each measurement tool. Our mapping of the definitions and measurement tools revealed numerous significant trends, clusters and differences between and within definitions and measurement tools focusing on hate crime, hate speech and hate incidents. For example, definitions and measurement tools tend to focus more on ethnic and religious identities (e.g., racism, antisemitism, Islamophobia) compared to sexual, gender and disability-related identities. This gap is greater in the definitions and measurement tools of hate speech than hate crime. Our analysis showed geographical patterns: hate crime definitions and measurement tools are more likely to originate from Anglophonic countries, especially the USA, but hate speech definitions and measurement tools are more likely to originate from continental Europe. In terms of disciplinary fragmentation, our social network analysis revealed that the collaboration and exchange of conceptual frameworks and methodological tools between social sciences and computer science is limited, with most definitions and measurement tools clustering along disciplinary lines. More detailed findings are presented in the results section of the report. Authors' Conclusions: There is an urgent need to close the research and policy gap between the protections of 'ethnic and religious identities' and other (less) protected characteristics such as gender and sexual identities, age and disability. There is also an urgent need to improve the quality of methodological and reporting standards in research examining hate behaviours, including transparency in methodology and data reporting, and discussion of limitations (e.g., bias in data). Many of the measurement tools found in the academic literature were excluded because they did not report transparently how they collected and analysed the data. Further, 41% of documents presenting research on hate behaviours did not provide a definition of what they were looking at. Given the importance of this policy domain, it is vital to raise the quality and trustworthiness of research in this area. This review found that researchers in different disciplinary areas (e.g., social sciences and computer science) rarely collaborate. Future research should attempt to build on existing definitions and measurement tools (instead of duplicating efforts), and engage in more interdisciplinary collaborations. It is our hope that that this review can provide a solid foundation for researchers, government, and other bodies to build cumulative knowledge and collaboration in this important field.

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.

Hate speech detection in the Arabic language: corpus design, construction, and evaluation.

Hate Speech Detection in Arabic presents a multifaceted challenge due to the broad and diverse linguistic terrain. With its multiple dialects and rich cultural subtleties, Arabic requires particular measures to address hate speech online successfully. To address this issue, academics and developers have used natural language processing (NLP) methods and machine learning algorithms adapted to the complexities of Arabic text. However, many proposed methods were hampered by a lack of a comprehensive dataset/corpus of Arabic hate speech. In this research, we propose a novel multi-class public Arabic dataset comprised of 403,688 annotated tweets categorized as extremely positive, positive, neutral, or negative based on the presence of hate speech. Using our developed dataset, we additionally characterize the performance of multiple machine learning models for Hate speech identification in Arabic Jordanian dialect tweets. Specifically, the Word2Vec, TF-IDF, and AraBert text representation models have been applied to produce word vectors. With the help of these models, we can provide classification models with vectors representing text. After that, seven machine learning classifiers have been evaluated: Support Vector Machine (SVM), Logistic Regression (LR), Naive Bays (NB), Random Forest (RF), AdaBoost (Ada), XGBoost (XGB), and CatBoost (CatB). In light of this, the experimental evaluation revealed that, in this challenging and unstructured setting, our gathered and annotated datasets were rather efficient and generated encouraging assessment outcomes. This will enable academics to delve further into this crucial field of study.

#ProtectOurElders: Analysis of Tweets About Older Asian Americans and Anti-Asian Sentiments During the COVID-19 Pandemic.

BACKGROUND: A silver lining to the COVID-19 pandemic is that it cast a spotlight on a long-underserved group. The barrage of attacks against older Asian Americans during the crisis galvanized society into assisting them in various ways. On Twitter, now known as X, support for them coalesced around the hashtag #ProtectOurElders. To date, discourse surrounding older Asian Americans has escaped the attention of gerontologists-a gap we seek to fill. Our study serves as a reflection of the level of support that has been extended to older Asian Americans, even as it provides timely insights that will ultimately advance equity for them. OBJECTIVE: This study explores the kinds of discourse surrounding older Asian Americans during the COVID-19 crisis, specifically in relation to the surge in anti-Asian sentiments. The following questions guide this study: What types of discourse have emerged in relation to older adults in the Asian American community and the need to support them? How do age and race interact to shape these discourses? What are the implications of these discourses for older Asian Americans? METHODS: We retrieved tweets (N=6099) through 2 search queries. For the first query, we collated tweets with the hashtag #ProtectOurElders. For the second query, we collected tweets with an age-based term, for example, "elderly" or "old(er) adults(s)" and either the hashtag #StopAAPIHate or #StopAsianHate. Tweets were posted from January 1, 2020, to August 1, 2023. After applying the exclusion criteria, the final data set contained 994 tweets. Inductive and deductive approaches informed our qualitative content analysis. RESULTS: A total of 4 themes emerged, with 50.1% (498/994) of posts framing older Asian Americans as "vulnerable and in need of protection" (theme 1). Tweets in this theme either singled them out as a group in need of protection because of their vulnerable status or discussed initiatives aimed at safeguarding their well-being. Posts in theme 2 (309/994, 31%) positioned them as "heroic and resilient." Relevant tweets celebrated older Asian Americans for displaying tremendous strength in the face of attack or described them as individuals not to be trifled with. Tweets in theme 3 (102/994, 10.2%) depicted them as "immigrants who have made selfless contributions and sacrifices." Posts in this section referenced the immense sacrifices made by older Asian Americans as they migrated to the United States, as well as the systemic barriers they had to overcome. Posts in theme 4 (85/994, 8.5%) venerated older Asian Americans as "worthy of honor." CONCLUSIONS: The COVID-19 crisis had the unintended effect of garnering greater support for older Asian Americans. It is consequential that support be extended to this group not so much by virtue of their perceived vulnerability but more so in view of their boundless contributions and sacrifices.

The Angry Echo Chamber: A Study of Extremist and Emotional Language Changes in Incel Communities Over Time.

Involuntary celibates, or incels, are part of a growing online subculture. Incels are men who are unable to engage in a sexual relationship with a woman and who experience significant distress and anger as a result. In recent years, high-profile incidents of violence perpetrated by incels or those who share incel ideology have increased research attention. Incels communicate online and share several characteristics with other online extremist groups. While only a fraction of incels engage in such violence, a broader spectrum of violence should be considered, including online harassment or general violence against women. This study sought to examine how ongoing engagement on an online incel forum affects changes in incel comments in terms of expressed anger and sadness and use of incel violent extremist language. We collected comments made on an incel forum over a 3-month period. We then identified prolific users and included their comments in our analysis. To assess how their language changed, we used a text-processing program (LIWC: Linguistic Inquiry and Word Count) to assess the extent to which anger, sadness, and incel violent extremist language were expressed in the comments. Our findings indicated that incels express more anger in their comments than users on other platforms such as Reddit, Facebook, and Twitter. However, they did not express greater sadness. Further, we found that incels are already quite angry and sad when they join the forum, and they already use a fair amount of incel vocabulary. Initially, these aspects of their language increase, but they flatten over time. This pattern suggests that introduction to and embracing of incel ideology occurs elsewhere on the Internet, and prior to people joining an incel forum. Implications in terms of prevention of online radicalization and future directions are discussed.

Individual and Contextual Correlates of Latent Bystander Profiles toward Racist Hate Speech: A Multilevel Person-centered Approach.

Prior research into bystander responses to hate speech has utilized variable-centered analyses - such approaches risk simplifying the complex nature of bystander behaviors. Hence, the present study used a person-centered analysis to investigate latent hate speech bystander profiles. In addition, individual and classroom-level correlates associated with the various profiles were studied. The sample included 3225 students in grades 7-9 (51.7% self-identified as female; 37.2% with immigrant background) from 215 classrooms in Germany and Switzerland. The latent profile analysis revealed that four distinct profiles could be distinguished: Passive Bystanders (34.2%), Defenders (47.3%), Revengers (9.8%), and Contributors (8.6%). Multilevel logistic regression models showed common and distinct correlates. For example, students who believed that certain social groups are superior were more likely to be Revengers and Contributors than Passive Bystanders, students who felt more connected with teachers were more likely to be Defenders, and students who were more open to diversity were less likely to be Contributors than Passive Bystanders. Students were less likely Defenders and more likely Revengers and Contributors than Passive Bystanders in classrooms with high rates of hate speech perpetration. Further, in classrooms with high hate speech intervention, students were more likely to be Defenders and less likely to be Contributors than Passive Bystanders. In classrooms with stronger cohesion, students were more likely to be Defenders and less likely to be Contributors than Passive Bystanders. In conclusion, the findings add to our understanding of bystander profiles concerning racist hate speech and the relevance of individual and classroom-level factors in explaining various profiles of bystander behavior.

Extending the Shadow of Sexual Assault Hypothesis: Fear of Sexual Violence and Hate Crimes among Lesbian, Gay, Bisexual, and Transgender Persons.

Women's fear has been explained as rooted in fears of sexual assault-a phenomenon known as the shadow of sexual assault hypothesis. The current study extends this hypothesis to examine whether lesbian, gay, bisexual, and transgender persons' fear of hate crimes is shadowed by fears of sexual assault. Results indicate that bisexual and transgender persons express greater fear of hate crimes relative to others. This fear is explained by their fear of sexual assault-supporting the shadow hypothesis for bisexual and transgender persons. Findings suggest the importance of fear of sexual assault in explaining sexual and gender minorities' fear of hate crimes.

The association between exposure to hate speech or perceived discrimination and mental health problems among Korean residents in Japan.

This study evaluated the effect of perceived discrimination and racism on the mental health state of Korean residents in Japan, with a particular focus on the risk of post-traumatic stress disorder (PTSD), depression, and psychological distress. Surveys were sent to Korean residents in Japan and a total of 240 valid responses were received. The valid response rate was 27.1%. The participants answered several questionnaire items, including demographic information and questions pertaining to their experiences of perceived discrimination, along with three self-reported measures of mental health, i.e., the Japanese version of Impact of Event Scale-Revised, the Zung Self-rating Depression Scale (SDS), and the 12-item General Health Questionnaire (GHQ-12). The results indicated that Korean residents in Japan experience hate speech and discrimination with a markedly high frequency (92.9% and 100%, respectively), and that factors such as employment discrimination and exposure to hate speech via social networking services were significant predictors of probable PTSD and psychological distress.

Prototypes of Hate and Expectations of the Model Victim.

This research explored the content of hate crime prototypes in a North American context, with particular attention to how such prototypes might influence blame attributions. In Study 1a, participants were recruited from a blended sample of universities (n = 110) and community members (n = 102) and asked to report their thoughts about typical hate crime offenses, victims, and offenders. These open-ended responses were coded, and common themes were identified. In Study 1b, a new group of participants (n = 290) were presented with these themes and asked to rate each for their characteristics of hate crimes. Studies 1a and 1b confirmed the presence of a clear prototype of hate crimes, such that (a) perpetrators were believed to be lower status White men with clear expressions of bias, (b) hate crime offenses were believed to be acts of interpersonal violence accompanied by slurs or verbal abuse, and (c) hate crime victims were thought to be members of a marginalized group who remain passive during the offense. Study 2 explored the consequences of victim prototypes on assessments of victim blame. Participants (n = 296) were recruited from York University and presented with a case vignette that varied the prototypicality of a victim of hate, depicting him as either Black or White and either passive, verbally responsive, or physically confrontational in the context of an assault. Participants showed greatest sympathy for the Black victim who passively ignored verbal harassment but increasingly assigned blame when the Black victim spoke or reacted physically. When the victim was White, participants showed little variation in their assessment of blame as a function of the victim's behavior. These results suggest that Black victims are subjected to greater behavioral scrutiny than White victims and that sympathy for victims of hate may be contingent on their passivity in the face of harassment.