Predicting Anti-Asian Hateful Users on Twitter during COVID-19
- URL: http://arxiv.org/abs/2109.07296v1
- Date: Wed, 15 Sep 2021 13:49:37 GMT
- Title: Predicting Anti-Asian Hateful Users on Twitter during COVID-19
- Authors: Jisun An, Haewoon Kwak, Claire Seungeun Lee, Bogang Jun, Yong-Yeol Ahn
- Abstract summary: We apply natural language processing techniques to characterize social media users who began to post anti-Asian hate messages during COVID-19.
It is possible to predict who later publicly posted anti-Asian slurs.
- Score: 7.788173128266611
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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