@inproceedings{markov-daelemans-2022-role,
title = "The Role of Context in Detecting the Target of Hate Speech",
author = "Markov, Ilia and
Daelemans, Walter",
editor = "Kumar, Ritesh and
Ojha, Atul Kr. and
Zampieri, Marcos and
Malmasi, Shervin and
Kadar, Daniel",
booktitle = "Proceedings of the Third Workshop on Threat, Aggression and Cyberbullying (TRAC 2022)",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.trac-1.5",
pages = "37--42",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T The Role of Context in Detecting the Target of Hate Speech
%A Markov, Ilia
%A Daelemans, Walter
%Y Kumar, Ritesh
%Y Ojha, Atul Kr.
%Y Zampieri, Marcos
%Y Malmasi, Shervin
%Y Kadar, Daniel
%S Proceedings of the Third Workshop on Threat, Aggression and Cyberbullying (TRAC 2022)
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F markov-daelemans-2022-role
%X 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.
%U https://aclanthology.org/2022.trac-1.5
%P 37-42
Markdown (Informal)
[The Role of Context in Detecting the Target of Hate Speech](https://aclanthology.org/2022.trac-1.5) (Markov & Daelemans, TRAC 2022)
ACL