The Role of Context in Detecting the Target of Hate Speech

Ilia Markov, Walter Daelemans


Abstract
Online hate speech detection is an inherently challenging task that has recently received much attention from the natural language processing community. Despite a substantial increase in performance, considerable challenges remain and include encoding contextual information into automated hate speech detection systems. In this paper, we focus on detecting the target of hate speech in Dutch social media: whether a hateful Facebook comment is directed against migrants or not (i.e., against someone else). We manually annotate the relevant conversational context and investigate the effect of different aspects of context on performance when adding it to a Dutch transformer-based pre-trained language model, BERTje. We show that performance of the model can be significantly improved by integrating relevant contextual information.
Anthology ID:
2022.trac-1.5
Volume:
Proceedings of the Third Workshop on Threat, Aggression and Cyberbullying (TRAC 2022)
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
TRAC
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
37–42
Language:
URL:
https://aclanthology.org/2022.trac-1.5
DOI:
Bibkey:
Cite (ACL):
Ilia Markov and Walter Daelemans. 2022. The Role of Context in Detecting the Target of Hate Speech. In Proceedings of the Third Workshop on Threat, Aggression and Cyberbullying (TRAC 2022), pages 37–42, Gyeongju, Republic of Korea. Association for Computational Linguistics.
Cite (Informal):
The Role of Context in Detecting the Target of Hate Speech (Markov & Daelemans, TRAC 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.trac-1.5.pdf
Data
Hate Speech