Predicting Anti-Asian Hateful Users on Twitter during COVID-19
Jisun An | Haewoon Kwak | Claire Seungeun Lee | Bogang Jun | Yong-Yeol Ahn
Findings of the Association for Computational Linguistics: EMNLP 2021
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.
SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment
Jisun An | Haewoon Kwak | Yong-Yeol Ahn
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Because word semantics can substantially change across communities and contexts, capturing domain-specific word semantics is an important challenge. Here, we propose SemAxis, a simple yet powerful framework to characterize word semantics using many semantic axes in word-vector spaces beyond sentiment. We demonstrate that SemAxis can capture nuanced semantic representations in multiple online communities. We also show that, when the sentiment axis is examined, SemAxis outperforms the state-of-the-art approaches in building domain-specific sentiment lexicons.