@inproceedings{tofa-etal-2025-cuet-novice,
title = "{CUET}{\_}{N}ovice@{D}ravidian{L}ang{T}ech 2025: Abusive Comment Detection in {M}alayalam Text Targeting Women on Social Media Using Transformer-Based Models",
author = "Tofa, Farjana Alam and
Sayma, Khadiza Sultana and
Osama, Md and
Dey, Ashim",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth and
Rajiakodi, Saranya and
Palani, Balasubramanian and
Subramanian, Malliga and
Cn, Subalalitha and
Chinnappa, Dhivya",
booktitle = "Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
month = may,
year = "2025",
address = "Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.dravidianlangtech-1.85/",
doi = "10.18653/v1/2025.dravidianlangtech-1.85",
pages = "483--488",
ISBN = "979-8-89176-228-2",
abstract = "Social media has become a widely used platform for communication and entertainment, but it has also become a space where abuseand harassment can thrive. Women, in particular, face hateful and abusive comments that reflect gender inequality. This paper discussesour participation in the Abusive Text Targeting Women in Dravidian Languages shared task at DravidianLangTech@NAACL 2025, whichfocuses on detecting abusive text targeting women in Malayalam social media comments. The shared task provided a dataset of YouTubecomments in Tamil and Malayalam, focusing on sensitive and controversial topics where abusive behavior is prevalent. Our participationfocused on the Malayalam dataset, where the goal was to classify comments into these categories accurately. Malayalam-BERT achievedthe best performance on the subtask, securing 3rd place with a macro f1-score of 0.7083, highlighting the effectiveness of transformer models for low-resource languages. These results contribute to tackling gender-based abuse and improving online content moderation for underrepresented languages."
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<abstract>Social media has become a widely used platform for communication and entertainment, but it has also become a space where abuseand harassment can thrive. Women, in particular, face hateful and abusive comments that reflect gender inequality. This paper discussesour participation in the Abusive Text Targeting Women in Dravidian Languages shared task at DravidianLangTech@NAACL 2025, whichfocuses on detecting abusive text targeting women in Malayalam social media comments. The shared task provided a dataset of YouTubecomments in Tamil and Malayalam, focusing on sensitive and controversial topics where abusive behavior is prevalent. Our participationfocused on the Malayalam dataset, where the goal was to classify comments into these categories accurately. Malayalam-BERT achievedthe best performance on the subtask, securing 3rd place with a macro f1-score of 0.7083, highlighting the effectiveness of transformer models for low-resource languages. These results contribute to tackling gender-based abuse and improving online content moderation for underrepresented languages.</abstract>
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%0 Conference Proceedings
%T CUET_Novice@DravidianLangTech 2025: Abusive Comment Detection in Malayalam Text Targeting Women on Social Media Using Transformer-Based Models
%A Tofa, Farjana Alam
%A Sayma, Khadiza Sultana
%A Osama, Md
%A Dey, Ashim
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%Y Rajiakodi, Saranya
%Y Palani, Balasubramanian
%Y Subramanian, Malliga
%Y Cn, Subalalitha
%Y Chinnappa, Dhivya
%S Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2025
%8 May
%I Association for Computational Linguistics
%C Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico
%@ 979-8-89176-228-2
%F tofa-etal-2025-cuet-novice
%X Social media has become a widely used platform for communication and entertainment, but it has also become a space where abuseand harassment can thrive. Women, in particular, face hateful and abusive comments that reflect gender inequality. This paper discussesour participation in the Abusive Text Targeting Women in Dravidian Languages shared task at DravidianLangTech@NAACL 2025, whichfocuses on detecting abusive text targeting women in Malayalam social media comments. The shared task provided a dataset of YouTubecomments in Tamil and Malayalam, focusing on sensitive and controversial topics where abusive behavior is prevalent. Our participationfocused on the Malayalam dataset, where the goal was to classify comments into these categories accurately. Malayalam-BERT achievedthe best performance on the subtask, securing 3rd place with a macro f1-score of 0.7083, highlighting the effectiveness of transformer models for low-resource languages. These results contribute to tackling gender-based abuse and improving online content moderation for underrepresented languages.
%R 10.18653/v1/2025.dravidianlangtech-1.85
%U https://aclanthology.org/2025.dravidianlangtech-1.85/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.85
%P 483-488
Markdown (Informal)
[CUET_Novice@DravidianLangTech 2025: Abusive Comment Detection in Malayalam Text Targeting Women on Social Media Using Transformer-Based Models](https://aclanthology.org/2025.dravidianlangtech-1.85/) (Tofa et al., DravidianLangTech 2025)
ACL
- Farjana Alam Tofa, Khadiza Sultana Sayma, Md Osama, and Ashim Dey. 2025. CUET_Novice@DravidianLangTech 2025: Abusive Comment Detection in Malayalam Text Targeting Women on Social Media Using Transformer-Based Models. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 483–488, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.