I Feel Offended, Don’t Be Abusive! Implicit/Explicit Messages in Offensive and Abusive Language

Tommaso Caselli, Valerio Basile, Jelena Mitrović, Inga Kartoziya, Michael Granitzer


Abstract
Abusive language detection is an unsolved and challenging problem for the NLP community. Recent literature suggests various approaches to distinguish between different language phenomena (e.g., hate speech vs. cyberbullying vs. offensive language) and factors (degree of explicitness and target) that may help to classify different abusive language phenomena. There are data sets that annotate the target of abusive messages (i.e.OLID/OffensEval (Zampieri et al., 2019a)). However, there is a lack of data sets that take into account the degree of explicitness. In this paper, we propose annotation guidelines to distinguish between explicit and implicit abuse in English and apply them to OLID/OffensEval. The outcome is a newly created resource, AbuseEval v1.0, which aims to address some of the existing issues in the annotation of offensive and abusive language (e.g., explicitness of the message, presence of a target, need of context, and interaction across different phenomena).
Anthology ID:
2020.lrec-1.760
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
6193–6202
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.760
DOI:
Bibkey:
Cite (ACL):
Tommaso Caselli, Valerio Basile, Jelena Mitrović, Inga Kartoziya, and Michael Granitzer. 2020. I Feel Offended, Don’t Be Abusive! Implicit/Explicit Messages in Offensive and Abusive Language. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 6193–6202, Marseille, France. European Language Resources Association.
Cite (Informal):
I Feel Offended, Don’t Be Abusive! Implicit/Explicit Messages in Offensive and Abusive Language (Caselli et al., LREC 2020)
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PDF:
https://aclanthology.org/2020.lrec-1.760.pdf