@inproceedings{g-etal-2025-linguaists-dravidianlangtech,
title = "{L}ingu{AI}sts@{D}ravidian{L}ang{T}ech 2025: Abusive {T}amil and {M}alayalam Text targeting Women on Social Media",
author = "G, Dhanyashree and
K, Kalpana and
A, Lekhashree and
K, Arivuchudar and
R, Arthi and
Sahitya, Bommineni and
J, Pavithra and
Johnson, Sandra",
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-1.116/",
doi = "10.18653/v1/2025.dravidianlangtech-1.116",
pages = "682--687",
ISBN = "979-8-89176-228-2",
abstract = "Social media sites are becoming crucial sites for communication and interaction, yet they are increasingly being utilized to commit gender-based abuse, with horrific, harassing, and degrading comments targeted at women. This paper tries to solve the common issue of women being subjected to abusive language in two South Indian languages, Malayalam and Tamil. To find explicit abuse, implicit bias, preconceptions, and coded language, we were given a set of YouTube comments labeled Abusive and Non-Abusive. To solve this problem, we applied and compared different machine learning models, i.e., Support Vector Machines (SVM), Logistic Regression (LR), and Naive Bayes classifiers, to classify comments into the given categories. The models were trained and validated using the given dataset to achieve the best performance with respect to accuracy and macro F1 score. The solutions proposed aim to make robust content moderation systems that can detect and prevent abusive language, ensuring safer online environments for women."
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<abstract>Social media sites are becoming crucial sites for communication and interaction, yet they are increasingly being utilized to commit gender-based abuse, with horrific, harassing, and degrading comments targeted at women. This paper tries to solve the common issue of women being subjected to abusive language in two South Indian languages, Malayalam and Tamil. To find explicit abuse, implicit bias, preconceptions, and coded language, we were given a set of YouTube comments labeled Abusive and Non-Abusive. To solve this problem, we applied and compared different machine learning models, i.e., Support Vector Machines (SVM), Logistic Regression (LR), and Naive Bayes classifiers, to classify comments into the given categories. The models were trained and validated using the given dataset to achieve the best performance with respect to accuracy and macro F1 score. The solutions proposed aim to make robust content moderation systems that can detect and prevent abusive language, ensuring safer online environments for women.</abstract>
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%0 Conference Proceedings
%T LinguAIsts@DravidianLangTech 2025: Abusive Tamil and Malayalam Text targeting Women on Social Media
%A G, Dhanyashree
%A K, Kalpana
%A A, Lekhashree
%A K, Arivuchudar
%A R, Arthi
%A Sahitya, Bommineni
%A J, Pavithra
%A Johnson, Sandra
%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 g-etal-2025-linguaists-dravidianlangtech
%X Social media sites are becoming crucial sites for communication and interaction, yet they are increasingly being utilized to commit gender-based abuse, with horrific, harassing, and degrading comments targeted at women. This paper tries to solve the common issue of women being subjected to abusive language in two South Indian languages, Malayalam and Tamil. To find explicit abuse, implicit bias, preconceptions, and coded language, we were given a set of YouTube comments labeled Abusive and Non-Abusive. To solve this problem, we applied and compared different machine learning models, i.e., Support Vector Machines (SVM), Logistic Regression (LR), and Naive Bayes classifiers, to classify comments into the given categories. The models were trained and validated using the given dataset to achieve the best performance with respect to accuracy and macro F1 score. The solutions proposed aim to make robust content moderation systems that can detect and prevent abusive language, ensuring safer online environments for women.
%R 10.18653/v1/2025.dravidianlangtech-1.116
%U https://aclanthology.org/2025.dravidianlangtech-1.116/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.116
%P 682-687
Markdown (Informal)
[LinguAIsts@DravidianLangTech 2025: Abusive Tamil and Malayalam Text targeting Women on Social Media](https://aclanthology.org/2025.dravidianlangtech-1.116/) (G et al., DravidianLangTech 2025)
ACL
- Dhanyashree G, Kalpana K, Lekhashree A, Arivuchudar K, Arthi R, Bommineni Sahitya, Pavithra J, and Sandra Johnson. 2025. LinguAIsts@DravidianLangTech 2025: Abusive Tamil and Malayalam Text targeting Women on Social Media. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 682–687, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.