@inproceedings{shimi-etal-2025-shimig,
title = "shimig@{D}ravidian{L}ang{T}ech2025: Stratification of Abusive content on Women in Social Media",
author = "Shimi, Gersome and
C, Jerin Mahibha and
Durairaj, Thenmozhi",
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-1.73/",
doi = "10.18653/v1/2025.dravidianlangtech-1.73",
pages = "409--414",
ISBN = "979-8-89176-228-2",
abstract = "The social network is a trending medium for interaction and sharing content globally. The content is sensitive since it can create an impact and change the trends of stakeholder{'}s thought as well as behavior. When the content is targeted towards women, it may be abusive or non-abusive and the identification is a tedious task. The content posted on social networks can be in English, code mix, or any low-resource language. The shared task Abusive Tamil and Malayalam Text targeting Women on Social Media was conducted as part of DravidianLangTech@NAACL 2025 organized by DravidianLangTech. The task is to identify the content given in Tamil or Malayalam or code mix as abusive or non-abusive. The task is accomplished for the South Indian languages Tamil and Malayalam using pretrained transformer model, BERT base multilingual cased and achieved the accuracy measure of 0.765 and 0.677."
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<abstract>The social network is a trending medium for interaction and sharing content globally. The content is sensitive since it can create an impact and change the trends of stakeholder’s thought as well as behavior. When the content is targeted towards women, it may be abusive or non-abusive and the identification is a tedious task. The content posted on social networks can be in English, code mix, or any low-resource language. The shared task Abusive Tamil and Malayalam Text targeting Women on Social Media was conducted as part of DravidianLangTech@NAACL 2025 organized by DravidianLangTech. The task is to identify the content given in Tamil or Malayalam or code mix as abusive or non-abusive. The task is accomplished for the South Indian languages Tamil and Malayalam using pretrained transformer model, BERT base multilingual cased and achieved the accuracy measure of 0.765 and 0.677.</abstract>
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%0 Conference Proceedings
%T shimig@DravidianLangTech2025: Stratification of Abusive content on Women in Social Media
%A Shimi, Gersome
%A C, Jerin Mahibha
%A Durairaj, Thenmozhi
%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 shimi-etal-2025-shimig
%X The social network is a trending medium for interaction and sharing content globally. The content is sensitive since it can create an impact and change the trends of stakeholder’s thought as well as behavior. When the content is targeted towards women, it may be abusive or non-abusive and the identification is a tedious task. The content posted on social networks can be in English, code mix, or any low-resource language. The shared task Abusive Tamil and Malayalam Text targeting Women on Social Media was conducted as part of DravidianLangTech@NAACL 2025 organized by DravidianLangTech. The task is to identify the content given in Tamil or Malayalam or code mix as abusive or non-abusive. The task is accomplished for the South Indian languages Tamil and Malayalam using pretrained transformer model, BERT base multilingual cased and achieved the accuracy measure of 0.765 and 0.677.
%R 10.18653/v1/2025.dravidianlangtech-1.73
%U https://aclanthology.org/2025.dravidianlangtech-1.73/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.73
%P 409-414
Markdown (Informal)
[shimig@DravidianLangTech2025: Stratification of Abusive content on Women in Social Media](https://aclanthology.org/2025.dravidianlangtech-1.73/) (Shimi et al., DravidianLangTech 2025)
ACL