A Just and Comprehensive Strategy for Using NLP to Address Online Abuse

David Jurgens, Libby Hemphill, Eshwar Chandrasekharan


Abstract
Online abusive behavior affects millions and the NLP community has attempted to mitigate this problem by developing technologies to detect abuse. However, current methods have largely focused on a narrow definition of abuse to detriment of victims who seek both validation and solutions. In this position paper, we argue that the community needs to make three substantive changes: (1) expanding our scope of problems to tackle both more subtle and more serious forms of abuse, (2) developing proactive technologies that counter or inhibit abuse before it harms, and (3) reframing our effort within a framework of justice to promote healthy communities.
Anthology ID:
P19-1357
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3658–3666
Language:
URL:
https://aclanthology.org/P19-1357
DOI:
10.18653/v1/P19-1357
Bibkey:
Cite (ACL):
David Jurgens, Libby Hemphill, and Eshwar Chandrasekharan. 2019. A Just and Comprehensive Strategy for Using NLP to Address Online Abuse. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3658–3666, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
A Just and Comprehensive Strategy for Using NLP to Address Online Abuse (Jurgens et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1357.pdf
Video:
 https://vimeo.com/385195473