Predicting Anti- A sian Hateful Users on T witter during COVID -19 (2021)

Abstract: 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.

## introduction the novel coronavirus pandemic (covid-19) provides a unique opportunity to study the development of targeted racial animus at an unprecedented scale. since the first known case of covid-19 was reported in wuhan, china @xcite , asians have been a target of online and offline hate. multiple surveys have shown an increase in anti-asian attitudes among americans, which has manifested in xenophobic behaviors, that range from not visiting asian restaurants or changing seats to avoiding asians in public places @xcite @xcite to verbal and physical harassment @xcite . online platforms, social media in particular, have been an exemplification, rather than an exception, of anti-asian hate, hosting plenty of hateful content. recent work has reported a striking increase of anti-asian slurs on twitter @xcite and negativity towards china on twitter and reddit @xcite . many asians have expressed an increased level of anxiety and depression due to the recent development of racial animus @xcite . as an attempt to investigate online hate towards asians during covid-19, several studies have introduced new datasets and methods for hate detection towards asians @xcite . while most studies focus on the content-e.g., whether a given text is a hate speech or not, userlevel analysis-e.g., whether a given user will post hateful content or not-is surprisingly underexplored, although this question can bring useful and important insights. first of all, considering the fact that only a handful of users produce the vast majority of misinformation (ccdh, 2021) and hateful content ( §4.2.1), focusing on users may be an efficient way to tackle online hate. for instance, identifying 'influential' users who tend to produce a large volume of such content and have the capacity to be the center of the discussion can lead to a better intervention than identifying individual instances of hate speech. second, understanding the risk factors for hate speech may also provide an opportunity to nudge the users and keep them from being radicalized. this pathway is only possible when we examine individual-level risk factors. finally, because there tends to be much more data available about individual users than individual tweets or posts @xcite , we may be able to develop a more accurate model as well as in-depth insights into the development pathways of online hate. at the same time, we emphasize that care must be taken when taking user-based approaches because the prediction of future offenses and misbehaviors can potentially lead to algorithmic bias as shown in the case of recidivism prediction @xcite . thus, our work should be considered an early step towards understanding online hate, and we caution that the translation of our results into practice would require much more nuanced investigation and decision-making. in this paper, we tackle online hate towards asians by focusing on users, particularly focusing on the risk indicators of hate towards asians. we examine users' language use in their tweets and information sources that they follow on social media. as polarization and echo chamber have been commonly observed on social media @xcite , it is reasonable to expect that users would choose to whom they listen, often in the way to strengthen their biases. thus we expect to see signals from the identities of the followings of users. although our retrospective case-control design does not allow strong causal inference (i.e., we cannot discern whether a user is affected by whom they follow or their following is a manifestation of their existing bias), our study may shed light on potential mechanisms and pathways towards online hate. also, while our work focuses on online hate, it would provide helpful insights for modeling offline hate crimes as well @xcite . our contributions can be summarized as twofold. first, we analyze user-level predictors of hate speech towards asians. we study the impact of both linguistic and information sources, namely (i) content features (social media posts published before covid-19) and (ii) content-agnostic features (what kind of information a user is exposed to, how a user interacts with the platform and other users, etc). we further study the level of expressed hate. second, we will release our dataset (features) and the code necessary to replicate our results foot_0 . ## related work racial animus has been widely studied from the perspective of its impact on individuals' health @xcite , economic development @xcite , voting @xcite and social unrest (bbc, 2020b). understanding how racial animus is developed is essential to prevent the risk of further intensifying racial animus and reduce its harm to society. online hater has been studied actively in recent years. on internet forum @xcite , wikipedia @xcite , news media comments @xcite , twitter @xcite @xcite , youtube @xcite , online games @xcite , and many other online platforms @xcite , different types of online hate have been investigated. while leveraging language to detect hate speech is not new @xcite , user-level modeling for detecting a future (event-driven) risk of posting hate speeches is relatively unexplored. among a few, @xcite identify linguistic markers that trigger toxicity in online discussions, @xcite detect the users who express online hate by checking their profiles or linguistic characteristics, @xcite incorporate users' historical tweets and user similarities for hate speech detection, and @xcite compares hate instigators, targets, and general twitter users by self-presentation, twitter visibility, and personality trait. @xcite characterize twitter users who use controversial terms associated with covid-19 (e.g., "chinese virus") and those who use non-controversial terms (e.g., "corona virus") in terms of demographics, profile features, political following, and geo-locations. unlike previous studies, our work focuses on not only user features but also on the content posted before they start to use asian slurs, studying the development of anti-asian hate attitudes. hate towards asians during covid-19 has appeared through diverse forms. for example, anti-asian slurs are increasingly used @xcite . also, over 40% of survey respondents in the u.s. would engage in discriminatory behavior towards asians due to fear of covid-19, lack of knowledge about the virus and trust in science, and more trust in donald trump @xcite . @xcite reported that xenophobic behavior as well as concerns about the virus is associated with anti-asian attitudes. the context and role of social media in online and offline hate have been argued to be important. croucher et al. (2020) examined a link between social media use and xenophobia toward asians. @xcite demonstrated that they could identify hate and counterhate tweets with an auroc of 0.85. @xcite built an annotated corpus for four asian prejudice-related classes, including criticism without being abusive. their best model achieved a macro-f1 score of 0.83, but the only 59.3% of hostility tweets were correctly identified. ## data we first identify those who express hate towards asians by collecting data from twitter in three steps: 1) compile a list of anti-asian slurs; 2) collect tweets with any of the anti-asian slurs; and 3) identify those who have used anti-asian slurs after the pandemic began (we call them hateful users) and collect their historical tweets from june 8, 2019 to may 8, 2020. then, as a control set, we randomly sample users who have tweets about covid-19 (we call them reference users) and collect their historical tweets during the same period. anti-asian slurs. we compile a list of anti-asian slurs by combining 1) wikipedia's list of ethnic slur words @xcite ) that includes 'chink,' 'chinazi,' and 'chicom,' and 2) a set of covid-19 specific anti-asian slurs, such as 'wuflu' and 'kungflu' @xcite . the full list of 33 anti-asian slurs is in appendix a. hateful users. using twint @xcite , in may 2020, we collect tweets that contain any of the anti-asian slurs from december 31, 2019 to may 1, 2020, resulting in 190,927 tweets posted by 120,690 users. we then consider those who 1) live in the u.s. and 2) posted at least two tweets with anti-asian slurs. we use self-declared locations in user profiles to infer their state-level location and exclude users without identified locations. this leaves us with 3,119 hateful users. reference users. as a control set, we construct a set of non-hate users. using twitter's streaming api, we collect 250m tweets that include covid-19 related keywords (e.g., "covid" and "coronavirus") from january 13 to april 12, 2020. the full list of covid-19 keywords used for this data collection can be found in appendix b. from this dataset, we randomly select 3,119 reference users, whose location can be detected at state-level and who have not used any anti-asian slurs. historical tweet collections. for the two user groups, we collect their historical tweets posted for 11 months from june 8, 2019 to may 8, 2020 using twint package @xcite . in total, we collect 18,952,895 tweets where 15.91m (83.94%) tweets of hateful users and 3.04m (16.06%) tweets of reference users. we find 15,728 tweets (0.00083%) containing anti-asian slurs. pre-and post-covid-19 tweets we use december 31, 2019 when china confirmed the first refining reference users. as explained above, our reference users are randomly selected from a large collection of covid-19-related tweets. we find that 19 reference users (0.6%) have used anti-asian slurs before covid-19. we exclude those users for the rest of the study. refining hateful users. some hateful users have used anti-asian slurs before covid-19. through manual examination, we notice that most of them are activists in hong kong, expressing negativity through slur words targeting china. we exclude these users from further analyses. by contrast, the use of slur words like "wuflu" and "kung flu" began to increase starting from march 9, 2020, a day when italy extends emergency measures nationwide (bbc, 2020a), and showed a sharp spike on march 16, 2020, when the former us president donald trump referred to covid-19 as "chinese virus" on twitter @xcite . since then, all anti-asian slurs becomes widely used. we focus on these users who turned hateful after the covid-19. low-vs high-level hateful users. we further divide hateful users into two groups based on the level of expressed haterism towards asians. in our data collection, there are 8,769 tweets that contain at least one slur word. the average number of tweets with a slur word per user is four (min: 1, median: 3, max: 126). thus, we further divide users into two groups based on the average tweets with slurs: low-level hateful users with less than or equal to three tweets with anti-asian slurs and high-level hateful users with at least four tweets with anti-asian slurs. bot detection. we further remove 'bot' accounts to better capture genuine human behaviors. we use botometer api @xcite , a popular supervised machine learning tool that checks twitter accounts for possibly automated activity by using features including content, network structure, and profile. given a twitter account, botometer api returns a score from 0 to 1, where 1 is the most likely to be a bot. we use the fifty-percent threshold (botometer @xmath0.5), which has proven effective in prior studies @xcite , to remove potential bot accounts. ## comparative exploration of hateful and reference users prior to predicting future hate behaviors of users, we first conduct a comparative exploration between hateful users and reference users to better understand their discriminating characteristics. ## predicting hateful users our comparative exploratory analysis indicates that there exist significant differences between these groups, raising an important question: can we predict those who turned hateful by only using the information available prior to the pandemic? the possibility of such prediction, combined with the analysis of prominent predictors, may help us understand the pathways into xenophobic haterism and design potential interventions. ## discussion and conclusion we presented a study on predicting users who would express hate towards asians during covid-19 by using their language use and information sources before covid-19. we modeled a user by a rich set of features derived from 1) contents published by the user and 2) content-agnostic dimensions, including their twitter statistics, profile description, and followings. for hateful user prediction task, our evaluation results showed that most features have a notable impact on prediction per-formance, which are tweets represented by sbert, followings, liwc, nela, shared news media, twitter statistics, and profile description (in this order). for high-level hateful user prediction, following features turn out to be more important than content features. moreover, embedding features are worse than nela or liwc features, indicating that linguistic styles and information sources are crucial for predicting levels of hate towards asians. our retrospective case-control design enabled us to study the distinctive features of hateful users in comparison with reference users. we reveal their individual importance and contribution, providing interpretable insights. in contrast to previous work focusing on user features on hate content, our study sheds light on potential mechanisms and pathways (risk factors) towards online hate. in particular, our finding that one feature, following, has a strong predictive power provides compelling sociological relevance. this finding hints at social factors (which communities they belong to) potentially being dominating factor in the development of racial hatred, suggesting a strong link between social polarization and xenophobia and calling for actions of social media companies and our society. there are some limitations to our work. first, this work is an observational study. since we model information sources based on shared urls and followings, we could not know information sources that are neither shared nor followed by users. second, as our study is based on the us and anti-asian behavior, further studies would require to generalize our findings for other countries or for other ethnic minorities. 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 @xcite @xcite , but also the population using asian slurs itself is of great importance because anti-asian slurs 1) are unambiguously pejorative @xcite and context-independent (hedger2010); 2) had not been commonly used unlike other racial slurs before the pandemic @xcite ; and 3) have not been reclaimed by the asian american community @xcite ; and 4) their prevalence has been linked to offline behaviors and hate crimes during covid-19 @xcite . 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. furthermore, this operationalization allows a highly transparent and unambiguous definition of the population. if we target the population with "hate towards asians," the operationalization would inevitably require adopting methods that are much more difficult to understand and evaluate. while the keyword-based method can result in a skewed dataset due to the oversampling of certain keywords (e.g., n-word) @xcite ), since we collect all tweets containing asian slurs to study users, not tweets, we believe the sampling bias would not be a critical issue in our study. furthermore, we would like to note that we exclude users who have used asian slurs before covid-19 to ensure that our data captures users who newly developed anti-asian attitudes. for future work, we plan to address the prediction task by ordinal regression that can inherently model the level of hate. we are also interested in characterizing hate towards asians in other languages. finally, we want to go beyond an observational study and attempt to find a potential causal relationship between information sources (biased, less credible information) and online hate. demographic information in our study. yet, we are able to predict whether an individual turns into hateful after the covid-19 at @xmath1.57% with simple machine learning methods based on publicly available twitter information. the risk of such user-based predictive tools for future offenses and misbehaviors is often underestimated and can potentially lead to algorithmic bias, as shown in the case of recidivism prediction @xcite . thus, we once again emphasize that our results should be considered as a first step for fighting online hate and further studies are required for translating it for practical use. this work is exempt from the requirement for irb review and approval (reference #11649). while sharing tweet ids is a common practice of studies using twitter data, there is a risk to share tweet ids in this work due to the sensitivity of the dataset. for example, even if a user in the "hateful" user set deleted all the hate tweets, others still may be able to see whether a particular user posted anti-asian slurs or not. this scenario may become possible if someone attempts to combine multiple tweet collections regarding covid-19. hence, we opt out of sharing tweet ids. instead, we share user-level features without revealing any personal information such as username, a profile description, etc.