From Fear to Hate: How the Covid-19 Pandemic Sparks Racial Animus in the United States

Lu, Runjing; Sheng, Yanying

doi:10.48550/arxiv.2007.01448

Cited by 4 publications

(6 citation statements)

References 21 publications

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“…Third, our target population is those who used anti-Asian slurs, and thus, other forms of anti-Asian hate may not be included in this work. However, we argue that not only that the keyword-based method is a widely adopted approach for studying anti-Asian attitudes during COVID-19 (Lu and Sheng, 2020;Schild et al, 2020;Lyu et al, 2020), but also the population using Asian slurs itself is of great importance because anti-Asian slurs 1) are unambiguously pejorative (Camp, 2013) and context-independent (Hedger2010); 2) had not been commonly used unlike other racial slurs before the pandemic (Schild et al, 2020); and 3) have not been reclaimed by the Asian American community (Croom, 2018); and 4) their prevalence has been linked to offline behaviors and hate crimes during COVID-19 (Lu and Sheng, 2020). Although the set of hateful content and the set of content with anti-Asian slurs would not completely overlap, we argue that because of the aforementioned reasons, our target population (those who used slurs instead of those who posted "hateful content") is still a useful operationalization of anti-Asian hate.…”

Section: Discussionmentioning

confidence: 97%

Predicting Anti-Asian Hateful Users on Twitter during COVID-19

Kwak

Lee

et al. 2021

Preprint

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We investigate predictors of anti-Asian hate among Twitter users throughout COVID-19. With the rise of xenophobia and polarization that has accompanied widespread social media usage in many nations, online hate has become a major social issue, attracting many researchers. Here, we apply natural language processing techniques to characterize social media users who began to post anti-Asian hate messages during COVID-19. We compare two user groups-those who posted anti-Asian slurs and those who did not-with respect to a rich set of features measured with data prior to COVID-19 and show that it is possible to predict who later publicly posted anti-Asian slurs. Our analysis of predictive features underlines the potential impact of news media and information sources that report on online hate and calls for further investigation into the role of polarized communication networks and news media.

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Section: Discussionmentioning

confidence: 97%

Predicting Anti-Asian Hateful Users on Twitter during COVID-19

Kwak

Lee

et al. 2021

Preprint

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“…Leveraging the large textual data corpus, studies have also used unsupervised techniques such as word2vec (Mikolov et al 2013), Glove embeddings (Pennington et al 2014) for topic clustering and keywords analysis. More recently, with the advent of deep learning and availability of training data, models such as long short-term memory networks (LSTMs) (Hochreiter and Schmidhuber 1997), RNNs, and much recently Transformers such as BERT (Devlin et al 2018) have been employed in most studies (He et al 2021;Lu and Sheng 2020) for classification of textual data on social media platforms.…”

Section: Bridging Social Science Theory and Computational Methodsmentioning

confidence: 99%

“…Unsupervised machine learning techniques such as topic modeling, keyword clustering have been widely employed in studies (e.g., (Tahmasbi et al, 2021)) for analysing Twitter and Reddit data during COVID-19. Scholars (He et al, 2021;Lu and Sheng, 2020) have also adopted supervised learning methods such as Support Vector Machines(SVMs) and Transformers for hate and racist speech detection.…”

Section: Introductionmentioning

confidence: 99%

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Multidimensional racism classification during COVID-19: stigmatization, offensiveness, blame, and exclusion

Pei

Mehta

2022

Soc. Netw. Anal. Min.

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Transcending the binary categorization of racist texts, our study takes cues from social science theories to develop a multidimensional model for racism detection, namely stigmatization, offensiveness, blame, and exclusion. With the aid of BERT and topic modelling, this categorical detection enables insights into the underlying subtlety of racist discussion on digital platforms during COVID-19. Our study contributes to enriching the scholarly discussion on deviant racist behaviours on social media. First, a stage-wise analysis is applied to capture the dynamics of the topic changes across the early stages of COVID-19 which transformed from a domestic epidemic to an international public health emergency and later to a global pandemic. Furthermore, mapping this trend enables a more accurate prediction of public opinion evolvement concerning racism in the offline world, and meanwhile, the enactment of specified intervention strategies to combat the upsurge of racism during the global public health crisis like COVID-19. In addition, this interdisciplinary research also points out a direction for future studies on social network analysis and mining. Integration of social science perspectives into the development of computational methods provides insights into more accurate data detection and analytics.

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“…Additionally, people holding these beliefs were found to be more likely to reject the COVID-19 vaccine, which is consistent with general work on conspiracy theories, misinformation and vaccine intention (Enders et al, 2020;Bertin et al, 2020;Romer & Jamieson, 2020;Lindholt et al, 2021;Jolley & Douglas, 2014). Finally, misinformation might have consequences beyond the realm of public health, as it might have also fueled racially charged attacks against those of Asian ancestry (Shahsavari et al, 2020;Lu & Sheng, 2020).…”

Section: Global Pandemic Global Conspiraciesmentioning

confidence: 99%

Who Shares Conspiracy Theories and Other Misinformation about Covid-19 Online: Survey Evidence from Five Countries

Pickup

Stecuła

Linden

2022

JQD

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Social media have long been considered a venue in which conspiracy theories and other misinformation incubate and spread. It has been no different during the COVID-19 pandemic. However, understanding who spreads misinformation by sharing it on social media, and why, has been underexplored, especially in a cross-national context. The global nature of the novel coronavirus pandemic presents an opportunity to understand the exposure and sharing of the same COVID-19 misinformation across multiple countries. We rely on nationally representative surveys conducted in July of 2020 and January of 2021 in the United States, United Kingdom, Canada, Australia, and New Zealand, to begin to understand what characterizes those who are most likely to share misinformation online. We find that Americans are no more likely to encounter prominent COVID-19 misinformation online but are considerably more likely to share it. Americans are less likely to say they share misinformation to make others aware of it or to criticize it, and considerably more likely to say their motivation is to promote it or to demonstrate their support for it. Americans are also more likely to say their motivation is to connect with others. In all countries but Canada, those who trust information from social media are more likely to share misinformation than those who do not trust social media. In all countries, those who have populist attitudes and distrust health officials are more likely to share misinformation than those who do not. In the U.S. in particular, sharing misinformation is associated with trust in government and identifying as conservative. Our results make clear that the United States is an outlier. We theorize why this might be the case.

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From Fear to Hate: How the Covid-19 Pandemic Sparks Racial Animus in the United States

Cited by 4 publications

References 21 publications

Predicting Anti-Asian Hateful Users on Twitter during COVID-19

Predicting Anti-Asian Hateful Users on Twitter during COVID-19

Multidimensional racism classification during COVID-19: stigmatization, offensiveness, blame, and exclusion

Who Shares Conspiracy Theories and Other Misinformation about Covid-19 Online: Survey Evidence from Five Countries

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