IDIAP Submission@LT-EDI-ACL2022: Homophobia/Transphobia Detection in social media comments

Muskaan Singh, Petr Motlicek


Abstract
The increased expansion of abusive content on social media platforms negatively affects online users. Transphobic/homophobic content indicates hatred comments for lesbian, gay, transgender, or bisexual people. It leads to offensive speech and causes severe social problems that can make online platforms toxic and unpleasant to LGBT+people, endeavoring to eliminate equality, diversity, and inclusion. In this paper, we present our classification system; given comments, it predicts whether or not it contains any form of homophobia/transphobia with a Zero-Shot learning framework. Our system submission achieved 0.40, 0.85, 0.89 F1-score for Tamil and Tamil-English, English with (1st, 1st,8th) ranks respectively. We release our codebase here: https://github.com/Muskaan-Singh/Homophobia-and-Transphobia-ACL-Submission.git.
Anthology ID:
2022.ltedi-1.55
Volume:
Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Bharathi Raja Chakravarthi, B Bharathi, John P McCrae, Manel Zarrouk, Kalika Bali, Paul Buitelaar
Venue:
LTEDI
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
356–361
Language:
URL:
https://aclanthology.org/2022.ltedi-1.55
DOI:
10.18653/v1/2022.ltedi-1.55
Bibkey:
Cite (ACL):
Muskaan Singh and Petr Motlicek. 2022. IDIAP Submission@LT-EDI-ACL2022: Homophobia/Transphobia Detection in social media comments. In Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pages 356–361, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
IDIAP Submission@LT-EDI-ACL2022: Homophobia/Transphobia Detection in social media comments (Singh & Motlicek, LTEDI 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.ltedi-1.55.pdf
Video:
 https://aclanthology.org/2022.ltedi-1.55.mp4
Code
 muskaan-singh/homophobia-and-transphobia-acl-submission