@inproceedings{sureshnathan-etal-2023-tercet,
title = "Tercet@{LT}-{EDI}-2023: Homophobia/Transphobia Detection in social media comment",
author = "Sureshnathan, Shwetha and
Sivakumar, Samyuktaa and
Thandavamurthi, Priyadharshini and
D., Thenmozhi and
B, Bharathi and
Chandrasekaran, Kiruthika",
editor = "Chakravarthi, Bharathi R. and
Bharathi, B. and
Griffith, Joephine and
Bali, Kalika and
Buitelaar, Paul",
booktitle = "Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.ltedi-1.41",
pages = "266--271",
abstract = "The advent of social media platforms has revo- lutionized the way we interact, share, learn , ex- press and build our views and ideas. One major challenge of social media is hate speech. Homo- phobia and transphobia encompasses a range of negative attitudes and feelings towards people based on their sexual orientation or gender iden- tity. Homophobia refers to the fear, hatred, or prejudice against homosexuality, while trans- phobia involves discrimination against trans- gender individuals. Natural Language Process- ing can be used to identify homophobic and transphobic texts and help make social media a safer place. In this paper, we explore us- ing Support Vector Machine , Random Forest Classifier and Bert Model for homophobia and transphobia detection. The best model was a combination of LaBSE and SVM that achieved a weighted F1 score of 0.95.",
}
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%0 Conference Proceedings
%T Tercet@LT-EDI-2023: Homophobia/Transphobia Detection in social media comment
%A Sureshnathan, Shwetha
%A Sivakumar, Samyuktaa
%A Thandavamurthi, Priyadharshini
%A D., Thenmozhi
%A B, Bharathi
%A Chandrasekaran, Kiruthika
%Y Chakravarthi, Bharathi R.
%Y Bharathi, B.
%Y Griffith, Joephine
%Y Bali, Kalika
%Y Buitelaar, Paul
%S Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F sureshnathan-etal-2023-tercet
%X The advent of social media platforms has revo- lutionized the way we interact, share, learn , ex- press and build our views and ideas. One major challenge of social media is hate speech. Homo- phobia and transphobia encompasses a range of negative attitudes and feelings towards people based on their sexual orientation or gender iden- tity. Homophobia refers to the fear, hatred, or prejudice against homosexuality, while trans- phobia involves discrimination against trans- gender individuals. Natural Language Process- ing can be used to identify homophobic and transphobic texts and help make social media a safer place. In this paper, we explore us- ing Support Vector Machine , Random Forest Classifier and Bert Model for homophobia and transphobia detection. The best model was a combination of LaBSE and SVM that achieved a weighted F1 score of 0.95.
%U https://aclanthology.org/2023.ltedi-1.41
%P 266-271
Markdown (Informal)
[Tercet@LT-EDI-2023: Homophobia/Transphobia Detection in social media comment](https://aclanthology.org/2023.ltedi-1.41) (Sureshnathan et al., LTEDI-WS 2023)
ACL
- Shwetha Sureshnathan, Samyuktaa Sivakumar, Priyadharshini Thandavamurthi, Thenmozhi D., Bharathi B, and Kiruthika Chandrasekaran. 2023. Tercet@LT-EDI-2023: Homophobia/Transphobia Detection in social media comment. In Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion, pages 266–271, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.