Attending the Emotions to Detect Online Abusive Language

Niloofar Safi Samghabadi, Afsheen Hatami, Mahsa Shafaei, Sudipta Kar, Thamar Solorio


Abstract
In recent years, abusive behavior has become a serious issue in online social networks. In this paper, we present a new corpus for the task of abusive language detection that is collected from a semi-anonymous online platform, and unlike the majority of other available resources, is not created based on a specific list of bad words. We also develop computational models to incorporate emotions into textual cues to improve aggression identification. We evaluate our proposed methods on a set of corpora related to the task and show promising results with respect to abusive language detection.
Anthology ID:
2020.alw-1.10
Volume:
Proceedings of the Fourth Workshop on Online Abuse and Harms
Month:
November
Year:
2020
Address:
Online
Venues:
ALW | EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
79–88
Language:
URL:
https://aclanthology.org/2020.alw-1.10
DOI:
10.18653/v1/2020.alw-1.10
Bibkey:
Cite (ACL):
Niloofar Safi Samghabadi, Afsheen Hatami, Mahsa Shafaei, Sudipta Kar, and Thamar Solorio. 2020. Attending the Emotions to Detect Online Abusive Language. In Proceedings of the Fourth Workshop on Online Abuse and Harms, pages 79–88, Online. Association for Computational Linguistics.
Cite (Informal):
Attending the Emotions to Detect Online Abusive Language (Safi Samghabadi et al., ALW 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.alw-1.10.pdf
Video:
 https://slideslive.com/38939534