@inproceedings{shanmugavadivel-etal-2024-kec,
title = "{KEC}-{AI}-{NLP}@{LT}-{EDI}-2024:Homophobia and Transphobia Detection in Social Media Comments using Machine Learning",
author = "Shanmugavadivel, Kogilavani and
Subramanian, Malliga and
R, Shri and
S, Srigha and
K, Samyuktha and
K, Nithika",
editor = {Chakravarthi, Bharathi Raja and
B, Bharathi and
Buitelaar, Paul and
Durairaj, Thenmozhi and
Kov{\'a}cs, Gy{\"o}rgy and
Garc{\'\i}a Cumbreras, Miguel {\'A}ngel},
booktitle = "Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion",
month = mar,
year = "2024",
address = "St. Julian's, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.ltedi-1.23",
pages = "200--205",
abstract = "Our work addresses the growing concern of abusive comments in online platforms, particularly focusing on the identification of Homophobia and Transphobia in social media comments. The goal is to categorize comments into three classes: Homophobia, Transphobia, and non-anti LGBT+ comments. Utilizing machine learning techniques and a deep learning model, our work involves training on a English dataset with a designated training set and testing on a validation set. This approach aims to contribute to the understanding and detection of Homophobia and Transphobia within the realm of social media interactions. Our team participated in the shared task organized by LTEDI@EACL 2024 and secured seventh rank in the task of Homophobia/Transphobia Detection in social media comments in Tamil with a macro- f1 score of 0.315. Also, our run was submitted for the English language and secured eighth rank with a macro-F1 score of 0.369. The run submitted for Malayalam language securing fourth rank with a macro- F1 score of 0.883 using the Random Forest model.",
}
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<abstract>Our work addresses the growing concern of abusive comments in online platforms, particularly focusing on the identification of Homophobia and Transphobia in social media comments. The goal is to categorize comments into three classes: Homophobia, Transphobia, and non-anti LGBT+ comments. Utilizing machine learning techniques and a deep learning model, our work involves training on a English dataset with a designated training set and testing on a validation set. This approach aims to contribute to the understanding and detection of Homophobia and Transphobia within the realm of social media interactions. Our team participated in the shared task organized by LTEDI@EACL 2024 and secured seventh rank in the task of Homophobia/Transphobia Detection in social media comments in Tamil with a macro- f1 score of 0.315. Also, our run was submitted for the English language and secured eighth rank with a macro-F1 score of 0.369. The run submitted for Malayalam language securing fourth rank with a macro- F1 score of 0.883 using the Random Forest model.</abstract>
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%0 Conference Proceedings
%T KEC-AI-NLP@LT-EDI-2024:Homophobia and Transphobia Detection in Social Media Comments using Machine Learning
%A Shanmugavadivel, Kogilavani
%A Subramanian, Malliga
%A R, Shri
%A S, Srigha
%A K, Samyuktha
%A K, Nithika
%Y Chakravarthi, Bharathi Raja
%Y B, Bharathi
%Y Buitelaar, Paul
%Y Durairaj, Thenmozhi
%Y Kovács, György
%Y García Cumbreras, Miguel Ángel
%S Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F shanmugavadivel-etal-2024-kec
%X Our work addresses the growing concern of abusive comments in online platforms, particularly focusing on the identification of Homophobia and Transphobia in social media comments. The goal is to categorize comments into three classes: Homophobia, Transphobia, and non-anti LGBT+ comments. Utilizing machine learning techniques and a deep learning model, our work involves training on a English dataset with a designated training set and testing on a validation set. This approach aims to contribute to the understanding and detection of Homophobia and Transphobia within the realm of social media interactions. Our team participated in the shared task organized by LTEDI@EACL 2024 and secured seventh rank in the task of Homophobia/Transphobia Detection in social media comments in Tamil with a macro- f1 score of 0.315. Also, our run was submitted for the English language and secured eighth rank with a macro-F1 score of 0.369. The run submitted for Malayalam language securing fourth rank with a macro- F1 score of 0.883 using the Random Forest model.
%U https://aclanthology.org/2024.ltedi-1.23
%P 200-205
Markdown (Informal)
[KEC-AI-NLP@LT-EDI-2024:Homophobia and Transphobia Detection in Social Media Comments using Machine Learning](https://aclanthology.org/2024.ltedi-1.23) (Shanmugavadivel et al., LTEDI-WS 2024)
ACL
- Kogilavani Shanmugavadivel, Malliga Subramanian, Shri R, Srigha S, Samyuktha K, and Nithika K. 2024. KEC-AI-NLP@LT-EDI-2024:Homophobia and Transphobia Detection in Social Media Comments using Machine Learning. In Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion, pages 200–205, St. Julian's, Malta. Association for Computational Linguistics.