Multiplex Anti- A sian Sentiment before and during the Pandemic: Introducing New Datasets from T witter Mining (2022)

Abstract: COVID-19 has disproportionately threatened minority communities in the U.S, not only in health but also in societal impact. However, social scientists and policymakers lack critical data to capture the dynamics of the anti-Asian hate trend and to evaluate its scale and scope. We introduce new datasets from Twitter related to anti-Asian hate sentiment before and during the pandemic. Relying on Twitter’s academic API, we retrieve hateful and counter-hate tweets from the Twitter Historical Database. To build contextual understanding and collect related racial cues, we also collect instances of heated arguments, often political, but not necessarily hateful, discussing Chinese issues. We then use the state-of-the-art hate speech classifiers to discern whether these tweets express hatred. These datasets can be used to study hate speech, general anti-Asian or Chinese sentiment, and hate linguistics by social scientists as well as to evaluate and build hate speech or sentiment analysis classifiers by computational scholars.

## introduction the covid-19 pandemic has disproportionately threatened minority communities in the u.s. in particular, covid-19 has brought sinophobia to the surface @xcite @xcite . since the outbreak of covid-19, there were over 4,000 hate incidents such as harassment and physical attacks reported to stopaapihate.org. the growing anti-asian attacks have led to the recent passage of the anti-asian hate crimes bill by the u.s. house after the mass shootings in atlanta. despite the problematic surge in covidhate incidents and crimes targeting asian american and pacific islander (aapi) communities, social scientists and policymakers lack critical data and quantitative measures to capture the evolution of anti-aapi trends in the u.s., and cannot evaluate the scale and scope of anti-aapi hate incidents in the pandemic. recent scholars have used social media data with machine learning techniques to track online anti-asian hate speech @xcite @xcite . for instance, @xcite examined the evolution and spread of anti-asian hate speech from 30 million tweets collected between january 15 and april 17, 2020. @xcite classified over 297 million tweets about china or covid-19 between january 2017 and june 2020 by using a bert model trained on 5000 labeled tweets and found that the awareness of covid-19 has led to a sharp rise in anti-china sentiments in the u.s. although these studies provide training datasets to build hate speech classifiers and have insights about the spread of anti-asian hate at the early stage of the outbreak, little is known about the enduring evolution of anti-asian hate or counter hate before and during the continuing pandemic. in this paper, we report trends and patterns of anti-asian sentiments and hate speech on twitter by introducing new datasets. twitter has been one of the most salient battlegrounds of both propagating and fighting against misinformation, fake news, hatred, and xenophobia during the covid-19 pandemic. we use computational tools with natural language processing and machine learning methods to detect hate speech on twitter before and during the pandemic. our datasets contain 68.38 million tweets, and they fall in four categories: the topics per se may not be hateful, but they often provoke hateful tweets such as discussions about uyghers, hong kong protests, and xi jinping these datasets provide a comprehensive portrait of the dynamics of anti-asian hate sentiments spanning from 2007 to 2021. thus, we are able to address important questions related to the evolution of anti-asian hate sentiments over time. ## background in the past decade, computational scholars have made great efforts to detect hate speech on social media platforms @xcite @xcite . although there is no clear and formal definition of hate speech, scholars tend to define hate speech as language or speech "used to express hatred towards a targeted group or is intended to be derogatory, to humiliate, or to insult the members of the group" @xcite . anti-asian hate speech, especially sinophobia, has attracted numerous attention from both computer scientists and social scientists @xcite @xcite . anti-asian hate speech could be understood as any speech that targets asians and people of asian descendants in a way that elevates hatred, violence, or social disorder. scapegoating immigrants in public crises is not unique to asians during the covid-19 pandemic, but has a long tradition in american history @xcite @xcite . for instance, tuberculosis was called "the jewish disease" in the 1890s and italian immigrants were blamed for the 1916 polio epidemic. chinese immigrants were blamed for the spread of smallpox in san francisco in the later 1870s and the spread of sars in 2003 @xcite . now asian immigrants, specifically chinese immigrants, are scapegoated for the origin and spread of covid-19. since the pandemic, around 2 million asian american adults have experienced various forms of anti-asian hate incident such as being beaten, spit on, and harassed based on aapi data reports @xcite . federal-level hate crime data also shows that anti-asian hate crimes increased by 149% in 16 major cities while hate crimes, in general, decreased by 7%. when former president trump singled out china as the origin of the pandemic and publicly used derogatory terms such as "chinese virus" and "kung flu," asian americans, particularly immigrants of chinese descendent, became the primary target of anti-asian hate crimes @xcite . anti-asian bigotry, violence, and misogyny were ingrained in american history following the passage of the page act of 1875 and the 1882 chinese exclusion act which prohibited the importation of chinese laborers and women @xcite . asian americans were often perceived as "disease, unassimilable aliens, and economic, cultural, and moral threats to a free white republic" @xcite . even today, asian americans, particularly women of asian descendants, are still the victims of anti-asian misogyny and hatred (e.g., the atlanta mass shooting in march 2021). 1 even before the covid-19 outbreak, hate crimes against asians increased by 31% in 2016-2018 under the trump administration @xcite . ## data to understand the anti-asian hate sentiments before and in the pandemic, we use covid specific hate terms, general anti-asian hate terms, anti-chinese politics terms, and counter-hate terms to extract all relevant tweets from twitter's historical database. this allows us to obtain a holistic view of multiplex anti-china or asian hate sentiment. here we briefly describe how we built our datasets and more information can be found in the appendix. since online hate speech mainly targets china, we focus on these anti-china related keywords in the present study. in future works, we wish to extend this study to other asian countries. covid-specific hate data first, we use keywords, such as 'chinese virus', 'china virus', 'wuhan virus', 'kungflu', and their variants, to extract all relevant tweets that target aapi communities. next, we use ziems et al.'s classifier to identify and exclude all these counter-hate tweets. ## results in this section, we report the major trends and patterns from our four main datasets. we start with covid19-specific hate data. ## conclusion this paper introduces new datasets to study anti-asian hate speech and sentiment on twitter before and during the pandemic. we show that the overall anti-chinese/asian hate sentiments were consistent before and after the outbreak of the covid-19 pandemic, but the pandemic threat has engendered the rise of covid-19 specific hate sentiments on twitter. hate speech online is a multiplex phenomenon. we built our datasets using keywords related to covid-19 specific hate terms, general anti-aapi hate terms, and anti-chinese politics terms as well as counter hate terms. as shown in our main analysis, we demonstrate that we can use these datasets to illustrate the overall trends and patterns of anti-asian hate speech online, and use aggregate statistics to demonstrate and describe the rise in anti-aapi hate speech during the covid era. researchers can also use these datasets to study how twitter users are radicalized by engaging into controversial conversations or what the linguistic features of hate speech on twitter are. we also provide the baseline tweets for researchers to normalize the trend of our datasets as shown in figure 7 . researchers can also use the de-identified author ids and conversation ids (which is unique ids for all tweets within the same reply thread and reply threads that are created from earlier reply threads) to conduct conversation network analysis. future users should be aware of possible underreporting due to many blatantly abusive tweets already being removed. still, our novel datasets can contribute to research in the areas of computational social science, machine learning, and hate speech detection.