This cross-sectional, descriptive study conducted in January 2022 reviewed 100 TikTok videos using the hashtag #StopAsianHate. Categoriesof Asian and Asian American (referred to hereafter as Asian) abuse/attack (N = 57) and awareness of Asian hate & hate crimes (N = 52) were observed in over 50% of videos. The following themes were of significance: Asian abuse/attack (p = .0079), awareness of Asian hate and hate crimes (p < .0001), somber tone/expression of sadness (p = .0025), stop Asian hate messages (p = .0380), media report of Asian hate crime (p =.0004), and mention of COVID/virus is hate p=.0103). Thus, the videos which raised awareness and specifically focused on abuse were more likely to be shared. TikTok is being used as a space for marginalized groups to raise consciousness on public health issues and injustices. These insights can potentially inform health communication efforts, cultural competency training, and targeted mental health support to address health equity and improve public health outcomes of Asian.

Hate crimes and hateful rhetoric targeting individuals of Asian descent have increased since the outbreak of COVID-19. These troubling trends have heightened concerns about the role of the Internet in facilitating radicalization. This article explores the existence of three warning signs of radicalization-fixation, group identification, and energy bursts-using data from Twitter and Reddit. Data were collected before and after the outbreak of COVID-19 to assess the role of the pandemic in affecting social media behavior. Using computational social science and Natural Language Processing techniques, we looked for signs of radicalization targeting China or Chinese individuals. Results show that fixation on the terms China and Chinese increased on Twitter and Reddit after the pandemic began. Moreover, tweets and posts containing either of these terms became more hateful, offensive, and negative after the outbreak. We also found evidence of individuals identifying more closely with a particular group, or adopting an \"us vs. them\" mentality, after the outbreak of COVID-19. These findings were especially prominent in subreddits catering to self-identified Republicans and Conservatives. Finally, we detected bursts of activity on Twitter and Reddit following the start of the pandemic. These warning signs suggest COVID-19 may have had a radicalizing effect on some social media users. This work is important because it not only shows the potential radicalizing effect of the pandemic, but also demonstrates the ability to detect warning signs of radicalization on social media. This is critical, as detecting warning signs of radicalization can potentially help curb hate-fueled violence.

BACKGROUND: Since the advent of the COVID-19 pandemic, individuals of Asian descent (colloquial usage prevalent in North America, where \"Asian\" is used to refer to people from East Asia, particularly China) have been the subject of stigma and hate speech in both offline and online communities. One of the major venues for encountering such unfair attacks is social networks, such as Twitter. As the research community seeks to understand, analyze, and implement detection techniques, high-quality data sets are becoming immensely important.
OBJECTIVE: In this study, we introduce a manually labeled data set of tweets containing anti-Asian stigmatizing content.
METHODS: We sampled over 668 million tweets posted on Twitter from January to July 2020 and used an iterative data construction approach that included 3 different stages of algorithm-driven data selection. Finally, we found volunteers who manually annotated the tweets by hand to arrive at a high-quality data set of tweets and a second, more sampled data set with higher-quality labels from multiple annotators. We presented this final high-quality Twitter data set on stigma toward Chinese people during the COVID-19 pandemic. The data set and instructions for labeling can be viewed in the Github repository. Furthermore, we implemented some state-of-the-art models to detect stigmatizing tweets to set initial benchmarks for our data set.
RESULTS: Our primary contributions are labeled data sets. Data Set v3.0 contained 11,263 tweets with primary labels (unknown/irrelevant, not-stigmatizing, stigmatizing-low, stigmatizing-medium, stigmatizing-high) and tweet subtopics (eg, wet market and eating habits, COVID-19 cases, bioweapon). Data Set v3.1 contained 4998 (44.4%) tweets randomly sampled from Data Set v3.0, where a second annotator labeled them only on the primary labels and then a third annotator resolved conflicts between the first and second annotators. To demonstrate the usefulness of our data set, preliminary experiments on the data set showed that the Bidirectional Encoder Representations from Transformers (BERT) model achieved the highest accuracy of 79% when detecting stigma on unseen data with traditional models, such as a support vector machine (SVM) performing at 73% accuracy.
CONCLUSIONS: Our data set can be used as a benchmark for further qualitative and quantitative research and analysis around the issue. It first reaffirms the existence and significance of widespread discrimination and stigma toward the Asian population worldwide. Moreover, our data set and subsequent arguments should assist other researchers from various domains, including psychologists, public policy authorities, and sociologists, to analyze the complex economic, political, historical, and cultural underlying roots of anti-Asian stigmatization and hateful behaviors. A manually annotated data set is of paramount importance for developing algorithms that can be used to detect stigma or problematic text, particularly on social media. We believe this contribution will help predict and subsequently design interventions that will significantly help reduce stigma, hate, and discrimination against marginalized populations during future crises like COVID-19.

Now mostly known as \"COVID-19\" (or simply \"Covid\"), early discourse around the pandemic was characterized by a particularly large variation in naming choices (ranging from \"new coronavirus\" and \"new respiratory disease\" to \"killer bug\" and the racist term \"Chinese virus\"). The current study is situated within corpus-assisted discourse studies and analyses these naming choices in UK newspaper coverage (January-March 2020), focusing on terminology deemed \"inappropriate\" as per WHO guidelines on naming infectious diseases. The results show that 9% of all terms referring to COVID-19 or the virus causing it are \"inappropriate\" overall, with \"inappropriate\" naming being more prevalent (1) in tabloids than broadsheets and (2) in the period before compared to the period after the virus was officially named on 11th February, 2020. Selected examples within each of the categories of \"inappropriate\" names are explored in more detail [terms (1) inciting undue fear, (2) containing geographic locations, and (3) containing species of animals], and the findings are discussed with regard to the contribution of lexical choices to the reproduction of (racist and otherwise problematic) ideologies in mainstream media.

Research has explored how the COVID-19 pandemic triggered a wave of conspiratorial thinking and online hate speech, but little is empirically known about how different phases of the pandemic are associated with hate speech against adversaries identified by online conspiracy communities. This study addresses this gap by combining observational methods with exploratory automated text analysis of content from an Italian-themed conspiracy channel on Telegram during the first year of the pandemic. We found that, before the first lockdown in early 2020, the primary target of hate was China, which was blamed for a new bioweapon. Yet over the course of 2020 and particularly after the beginning of the second lockdown, the primary targets became journalists and healthcare workers, who were blamed for exaggerating the threat of COVID-19. This study advances our understanding of the association between hate speech and a complex and protracted event like the COVID-19 pandemic, and it suggests that country-specific responses to the virus (e.g., lockdowns and re-openings) are associated with online hate speech against different adversaries depending on the social and political context.

Since the beginning of the COVID-19 pandemic, there have been widespread conversations about the origins of the virus and who to blame for it. This article focuses on the online hate directed at Chinese and Asian people during the pandemic. Taking a critical discursive psychological approach, we analysed seven online threads related to COVID-19 and China from two Finnish websites (Suomi24 and Ylilauta) and one US (8kun) site. We identified three discursive trends associated with dehumanising Chinese populations: \'monstrous Chinese\', \'immoral Chinese\' and \'China as a threat\', which created different forms of dehumanisation on a continuum from harsher dehumanisation to milder depersonalisation. The animalistic metaphors, coarse language, humorous frames and conspiracy beliefs worked to rhetorically justify the dehumanisation of Chinese individuals, making it more acceptable to portray them as a homogeneous and inhumane mass of people that deserves to be attacked. This study contributes to the field of discursive research on dehumanisation by deepening our knowledge of the specific features of Sinophobic hate speech.

Due to the geographic origins of the first major outbreak of COVID-19 in Wuhan, China, individuals of Chinese ethnic origin around the world have experienced discrimination, xenophobia, and racism during the pandemic. Discriminatory actions have ranged from outright physical aggression to subtle microaggressions. While reports (both media and academic) have highlighted such incidents, this paper argues that when the conversation starts and stops at the reporting of experiences of stigma, the narrative remains as the victimization of the community. Instead, instances of COVID-19 stigma and discrimination are only one aspect of this story, where other aspects include a deeper understanding of the community itself along with an awareness of the capacity that the Chinese diaspora community brings forward to help overcome COVID-19. We focus our discussion on the Greater Toronto Area (GTA) in Canada, a global urban center that has a sizeable ethnic Chinese diaspora community, and argue that highlighting the early actions that the community took to help broader society in dealing with COVID-19 at the start of the pandemic may help to reframe anti-Chinese stigma during the pandemic. These early actions include physical distancing, mask-wearing, sanitation and advocacy. Findings for this case-study are informed by media monitoring and interviews with 83 individuals identifying as ethnically Chinese living across the GTA.

The weaponization of racialized imagery has been a common feature of geopolitical contestation in contemporary history. The paper critically examines the historical genesis of Sinophobic narratives, which have been common features of the big power geopolitical contestation in the Pacific. The globalization of capitalism in the nineteenth century and the West\'s attempts to penetrate the Chinese market and exploitation of its resources led to tension, skirmishes and wars. The myth of racial European superiority and corresponding inferiority of the Chinese was weaponized as an ideological justification for colonial domination, exploitation of cheap labour and appropriation of China\'s resources and wealth. In recent years, the Sinophobic paranoia has been exacerbated by China\'s Belt and Road initiative, a strategy at global economic and technological supremacy to counter the West\'s dominance. This competition for global hegemony is played out in various parts of the world and the Pacific included. The paper critically discusses various historical factors associated with Sinophobia in the context of the USA, France and Australia and how these have influenced these countries\' contemporary approaches to Chinese expansionism.

Modern scholarship has drawn hasty and numerous parallels between the Yellow Peril discourses of the 19th- and 20th-century plagues and the recent racialization of infectious disease in the 21st-century. While highlighting these similarities is politically useful against Sinophobic epidemic narratives, Michel Foucault argues that truly understanding the past\'s continuity in the present requires a more rigorous genealogical approach. Employing this premise in a comparative analysis, this work demonstrates a critical discontinuity in the epidemic imaginary that framed the Chinese as pathogenic. Consequently, those seeking to prevent future disease racialization must understand modern Sinophobia as fundamentally distinct from that of the past.

When labeling an infectious disease, officially sanctioned scientific names, e.g., \"H1N1 virus,\" are recommended over place-specific names, e.g., \"Spanish flu.\" This is due to concerns from policymakers and the WHO that the latter might lead to unintended stigmatization. However, with little empirical support for such negative consequences, authorities might be focusing on limited resources on an overstated issue. This paper empirically investigates the impact of naming against the current backdrop of the 2019-2020 pandemic. The first hypothesis posited that using place-specific names associated with China (e.g., Wuhan Virus or China Virus) leads to greater levels of sinophobia, the negative stigmatization of Chinese individuals. The second hypothesis posited that using a scientific name (e.g., Coronavirus or COVID-19) leads to increased anxiety, risk aversion, beliefs about contagiousness of the virus, and beliefs about mortality rate. Results from two preregistered studies [N(Study 1) = 504; N(Study 2) = 412], conducted across three countries with the first study during the early outbreak (April 2020) and the second study at a later stage of the pandemic (August 2020), found no evidence of any adverse effects of naming on sinophobia and strong support for the null hypothesis using Bayesian analyses. Moreover, analyses found no impact of naming on anxiety, risk aversion, beliefs about contagiousness of the virus, or beliefs about mortality rate, with mild to strong support for the null hypothesis across outcomes. Exploratory analyses also found no evidence for the effect of naming being moderated by political affiliation. In conclusion, results provide no evidence that virus naming impacted individual\'s attitudes toward Chinese individuals or perceptions of the virus, with the majority of analyses finding strong support for the null hypothesis. Therefore, based on the current evidence, it appears that the importance given to naming infectious diseases might be inflated.