@inproceedings{vp-etal-2025-cyber,
title = "Cyber Protectors@{D}ravidian{L}ang{T}ech 2025: Abusive {T}amil and {M}alayalam Text Targeting Women on Social Media using {F}ast{T}ext",
author = "Vp, Rohit and
M, Madhav and
Srichandra, Ippatapu Venkata and
Mohan, Neethu and
S, Sachin Kumar",
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-1.53/",
doi = "10.18653/v1/2025.dravidianlangtech-1.53",
pages = "304--308",
ISBN = "979-8-89176-228-2",
abstract = "Social media has transformed communication, but it has opened new ways for women to be abused. Because of complex morphology, large vocabulary, and frequent code-mixing of Tamil and Malayalam, it might be especially challenging to identify discriminatory text in linguistically diverse settings. Because traditional moderation systems frequently miss these linguistic subtleties, gendered abuse in many forms{---}from outright threats to character insults and body shaming{---}continues. In addition to examining the sociocultural characteristics of this type of harassment on social media, this study compares the effectiveness of several Natural Language Processing (NLP) models, such as FastText, transformer-based architectures, and BiLSTM. Our results show that FastText achieved an macro f1 score of 0.74 on the Tamil dataset and 0.64 on the Malayalam dataset, outperforming the Transformer model which achieved a macro f1 score of 0.62 and BiLSTM achieved 0.57. By addressing the limitations of existing moderation techniques, this research underscores the urgent need for language-specific AI solutions to foster safer digital spaces for women."
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%0 Conference Proceedings
%T Cyber Protectors@DravidianLangTech 2025: Abusive Tamil and Malayalam Text Targeting Women on Social Media using FastText
%A Vp, Rohit
%A M, Madhav
%A Srichandra, Ippatapu Venkata
%A Mohan, Neethu
%A S, Sachin Kumar
%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 vp-etal-2025-cyber
%X Social media has transformed communication, but it has opened new ways for women to be abused. Because of complex morphology, large vocabulary, and frequent code-mixing of Tamil and Malayalam, it might be especially challenging to identify discriminatory text in linguistically diverse settings. Because traditional moderation systems frequently miss these linguistic subtleties, gendered abuse in many forms—from outright threats to character insults and body shaming—continues. In addition to examining the sociocultural characteristics of this type of harassment on social media, this study compares the effectiveness of several Natural Language Processing (NLP) models, such as FastText, transformer-based architectures, and BiLSTM. Our results show that FastText achieved an macro f1 score of 0.74 on the Tamil dataset and 0.64 on the Malayalam dataset, outperforming the Transformer model which achieved a macro f1 score of 0.62 and BiLSTM achieved 0.57. By addressing the limitations of existing moderation techniques, this research underscores the urgent need for language-specific AI solutions to foster safer digital spaces for women.
%R 10.18653/v1/2025.dravidianlangtech-1.53
%U https://aclanthology.org/2025.dravidianlangtech-1.53/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.53
%P 304-308
Markdown (Informal)
[Cyber Protectors@DravidianLangTech 2025: Abusive Tamil and Malayalam Text Targeting Women on Social Media using FastText](https://aclanthology.org/2025.dravidianlangtech-1.53/) (Vp et al., DravidianLangTech 2025)
ACL
- Rohit Vp, Madhav M, Ippatapu Venkata Srichandra, Neethu Mohan, and Sachin Kumar S. 2025. Cyber Protectors@DravidianLangTech 2025: Abusive Tamil and Malayalam Text Targeting Women on Social Media using FastText. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 304–308, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.