Unraveling Social Perceptions & Behaviors towards Migrants on Twitter
This work provides a nuanced understanding for researchers and practitioners in NLP by fine-tuning hate speech detection with granular distinctions, though it is incremental in applying existing methods to a specific domain.
The study tackled the problem of analyzing social perceptions and behaviors towards migrants on Twitter by identifying sympathy, antipathy, solidarity, and animosity, achieving an F1-score of 0.76 with a BERT+CNN model.
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 mi-grants. 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 unsuper-vised and supervised approaches to identify these perceptions and behaviors. In the domain of applied NLP, our study of-fers a nuanced understanding of migrant-related Twitter de-liberations. Our proposed transformer-based model, i.e., BERT + CNN, has reported an F1-score of 0.76 and outper-formed other models. Additionally, we argue that tweets con-veying 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.