Unraveling Social Perceptions & Behaviors towards Migrants on Twitter

Authors

  • Aparup Khatua L3S and Leibniz University of Hannover
  • Wolfgang Nejdl L3S and Leibniz University of Hannover

Keywords:

Subjectivity in textual data; sentiment analysis; polarity/opinion identification and extraction, linguistic analyses of social media behavior, Text categorization; topic recognition; demographic/gender/age identification, Psychological, personality-based and ethnographic studies of social media, Organizational and group behavior mediated by social media; interpersonal communication mediated by social media

Abstract

We draw insights from the social psychology literature to identify two facets of Twitter deliberations about migrants, i.e., perceptions about migrants and behaviors towards migrants. Our theoretical anchoring helped us in identifying two prevailing perceptions (i.e., sympathy and antipathy) and two dominant behaviors (i.e., solidarity and animosity) of social media users towards migrants. We have employed unsupervised and supervised approaches to identify these perceptions and behaviors. In the domain of applied NLP, our study offers a nuanced understanding of migrant-related Twitter deliberations. Our proposed transformer-based model, i.e., BERT + CNN, has reported an F1-score of 0.76 and outperformed other models. Additionally, we argue that tweets conveying antipathy or animosity can be broadly considered hate speech towards migrants, but they are not the same. Thus, our approach has fine-tuned the binary hate speech detection task by highlighting the granular differences between perceptual and behavioral aspects of hate speeches.

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Published

2022-05-31

How to Cite

Khatua, A., & Nejdl, W. (2022). Unraveling Social Perceptions & Behaviors towards Migrants on Twitter. Proceedings of the International AAAI Conference on Web and Social Media, 16(1), 512-523. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/19311