@inproceedings{sivagnanam-etal-2026-comments,
title = "From Comments to Harm: A Findings Report on Abusive {T}amil Text Targeting Women on Social Media Shared Task",
author = "Sivagnanam, Bhuvaneswari and
Pannerselvam, Kathiravan and
Jananayagan and
Rajkumar, Charmathi and
R, Ramesh Kannan and
Rajalakshmi, Ratnavel and
Chinnan, Shunmuga Priya Muthusamy and
Rajiakodi, Saranya and
Chakravarthi, Bharathi Raja",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Rajiakodi, Saranya and
Navaneethakrishnan, Subalalitha and
Chinnappa, Dhivya and
Palani, Balasubramanian and
Subramanian, Malliga and
Shanmugavadivel, Kogilavani and
Rajalakshmi, Ratnavel",
booktitle = "Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for {D}ravidian Languages",
month = jul,
year = "2026",
address = "Underline (Virtual)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.dravidianlangtech-1.11/",
pages = "88--100",
ISBN = "979-8-89176-401-9",
abstract = "This paper presents an overview of the second shared task on Abusive Tamil Text Targeting Women on Social Media as a binary classification problem (abusive vs. non-abusive). We release a dataset of Tamil YouTube comments and evaluate submissions using macro-F1 to encourage balanced performance in a noisy, low-resource setting. There are 89 teams registered for this task and 24 teams submitted the results. The approaches used by the teams includes transformer fine-tuning, heterogeneous ensembles, classical baselines, and large language models using prompting and LoRA. Results show that the best-performing system scored 0.8297 macro-F1 and many submissions are around 0.79-0.81. Across submissions, transformer fine-tuning with domain-aligned encoders is consistently strong, while additional gains are frequently associated with Tamil-aware normalization and macro-F1-oriented calibration such as class-weighted learning and validation-based threshold tuning. Overall, the findings highlights the importance of language-aware preprocessing and careful decision calibration for reliable moderation of women-targeted abusive Tamil social media text.Disclaimer: This paper (including figures and examples) may contain offensive or harmful language, including abusive content targeting women. All such text is presented solely for research and educational purposes and it does not reflect the author{'}s views. Reader discretion is advised."
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<abstract>This paper presents an overview of the second shared task on Abusive Tamil Text Targeting Women on Social Media as a binary classification problem (abusive vs. non-abusive). We release a dataset of Tamil YouTube comments and evaluate submissions using macro-F1 to encourage balanced performance in a noisy, low-resource setting. There are 89 teams registered for this task and 24 teams submitted the results. The approaches used by the teams includes transformer fine-tuning, heterogeneous ensembles, classical baselines, and large language models using prompting and LoRA. Results show that the best-performing system scored 0.8297 macro-F1 and many submissions are around 0.79-0.81. Across submissions, transformer fine-tuning with domain-aligned encoders is consistently strong, while additional gains are frequently associated with Tamil-aware normalization and macro-F1-oriented calibration such as class-weighted learning and validation-based threshold tuning. Overall, the findings highlights the importance of language-aware preprocessing and careful decision calibration for reliable moderation of women-targeted abusive Tamil social media text.Disclaimer: This paper (including figures and examples) may contain offensive or harmful language, including abusive content targeting women. All such text is presented solely for research and educational purposes and it does not reflect the author’s views. Reader discretion is advised.</abstract>
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%0 Conference Proceedings
%T From Comments to Harm: A Findings Report on Abusive Tamil Text Targeting Women on Social Media Shared Task
%A Sivagnanam, Bhuvaneswari
%A Pannerselvam, Kathiravan
%A Rajkumar, Charmathi
%A R, Ramesh Kannan
%A Rajalakshmi, Ratnavel
%A Chinnan, Shunmuga Priya Muthusamy
%A Rajiakodi, Saranya
%A Chakravarthi, Bharathi Raja
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Rajiakodi, Saranya
%Y Navaneethakrishnan, Subalalitha
%Y Chinnappa, Dhivya
%Y Palani, Balasubramanian
%Y Subramanian, Malliga
%Y Shanmugavadivel, Kogilavani
%Y Rajalakshmi, Ratnavel
%A Jananayagan
%S Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2026
%8 July
%I Association for Computational Linguistics
%C Underline (Virtual)
%@ 979-8-89176-401-9
%F sivagnanam-etal-2026-comments
%X This paper presents an overview of the second shared task on Abusive Tamil Text Targeting Women on Social Media as a binary classification problem (abusive vs. non-abusive). We release a dataset of Tamil YouTube comments and evaluate submissions using macro-F1 to encourage balanced performance in a noisy, low-resource setting. There are 89 teams registered for this task and 24 teams submitted the results. The approaches used by the teams includes transformer fine-tuning, heterogeneous ensembles, classical baselines, and large language models using prompting and LoRA. Results show that the best-performing system scored 0.8297 macro-F1 and many submissions are around 0.79-0.81. Across submissions, transformer fine-tuning with domain-aligned encoders is consistently strong, while additional gains are frequently associated with Tamil-aware normalization and macro-F1-oriented calibration such as class-weighted learning and validation-based threshold tuning. Overall, the findings highlights the importance of language-aware preprocessing and careful decision calibration for reliable moderation of women-targeted abusive Tamil social media text.Disclaimer: This paper (including figures and examples) may contain offensive or harmful language, including abusive content targeting women. All such text is presented solely for research and educational purposes and it does not reflect the author’s views. Reader discretion is advised.
%U https://aclanthology.org/2026.dravidianlangtech-1.11/
%P 88-100
Markdown (Informal)
[From Comments to Harm: A Findings Report on Abusive Tamil Text Targeting Women on Social Media Shared Task](https://aclanthology.org/2026.dravidianlangtech-1.11/) (Sivagnanam et al., DravidianLangTech 2026)
ACL
- Bhuvaneswari Sivagnanam, Kathiravan Pannerselvam, Jananayagan, Charmathi Rajkumar, Ramesh Kannan R, Ratnavel Rajalakshmi, Shunmuga Priya Muthusamy Chinnan, Saranya Rajiakodi, and Bharathi Raja Chakravarthi. 2026. From Comments to Harm: A Findings Report on Abusive Tamil Text Targeting Women on Social Media Shared Task. In Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 88–100, Underline (Virtual). Association for Computational Linguistics.