@inproceedings{an-etal-2021-predicting-anti,
title = "Predicting Anti-{A}sian Hateful Users on {T}witter during {COVID}-19",
author = "An, Jisun and
Kwak, Haewoon and
Lee, Claire Seungeun and
Jun, Bogang and
Ahn, Yong-Yeol",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.398",
doi = "10.18653/v1/2021.findings-emnlp.398",
pages = "4655--4666",
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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<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.</abstract>
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%0 Conference Proceedings
%T Predicting Anti-Asian Hateful Users on Twitter during COVID-19
%A An, Jisun
%A Kwak, Haewoon
%A Lee, Claire Seungeun
%A Jun, Bogang
%A Ahn, Yong-Yeol
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F an-etal-2021-predicting-anti
%X 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.
%R 10.18653/v1/2021.findings-emnlp.398
%U https://aclanthology.org/2021.findings-emnlp.398
%U https://doi.org/10.18653/v1/2021.findings-emnlp.398
%P 4655-4666
Markdown (Informal)
[Predicting Anti-Asian Hateful Users on Twitter during COVID-19](https://aclanthology.org/2021.findings-emnlp.398) (An et al., Findings 2021)
ACL
- Jisun An, Haewoon Kwak, Claire Seungeun Lee, Bogang Jun, and Yong-Yeol Ahn. 2021. Predicting Anti-Asian Hateful Users on Twitter during COVID-19. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4655–4666, Punta Cana, Dominican Republic. Association for Computational Linguistics.