TY - GEN

T1 - Unraveling Social Perceptions & Behaviors towards Migrants on Twitter

AU - Nejdl, Wolfgang

AU - Khatua, Aparup

N1 - Funding Information: Funding for this paper was, in part, provided by the Euro-pean Union’sHorizon 2020 research and innovation pro-gram under Grant Agreement No: 832921.

PY - 2022/5/31

Y1 - 2022/5/31

N2 - 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.

AB - 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.

U2 - 10.48550/arXiv.2112.06642

DO - 10.48550/arXiv.2112.06642

M3 - Conference contribution

SN - 978-1-57735-875-6

SN - 1-57735-875-9

T3 - Proceedings of the Sixteenth International AAAI Conference on Web and Social Media

SP - 512

EP - 523

BT - Proceedings of the Sixteenth International AAAI Conference on Web and Social Media (ICWSM2022)

ER -