GS_DravidianLangTech@2025: Women Targeted Abusive Texts Detection on Social Media
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912307921027072 |
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| author | Bade, Girma Yohannis Ahani, Zahra Kolesnikova, Olga Oropeza, José Luis Sidorov, Grigori |
| author_facet | Bade, Girma Yohannis Ahani, Zahra Kolesnikova, Olga Oropeza, José Luis Sidorov, Grigori |
| contents | The increasing misuse of social media has become a concern; however, technological solutions are being developed to moderate its content effectively. This paper focuses on detecting abusive texts targeting women on social media platforms. Abusive speech refers to communication intended to harm or incite hatred against vulnerable individuals or groups. Specifically, this study aims to identify abusive language directed toward women. To achieve this, we utilized logistic regression and BERT as base models to train datasets sourced from DravidianLangTech@2025 for Tamil and Malayalam languages. The models were evaluated on test datasets, resulting in a 0.729 macro F1 score for BERT and 0.6279 for logistic regression in Tamil and Malayalam, respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02863 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GS_DravidianLangTech@2025: Women Targeted Abusive Texts Detection on Social Media Bade, Girma Yohannis Ahani, Zahra Kolesnikova, Olga Oropeza, José Luis Sidorov, Grigori Computation and Language Social and Information Networks The increasing misuse of social media has become a concern; however, technological solutions are being developed to moderate its content effectively. This paper focuses on detecting abusive texts targeting women on social media platforms. Abusive speech refers to communication intended to harm or incite hatred against vulnerable individuals or groups. Specifically, this study aims to identify abusive language directed toward women. To achieve this, we utilized logistic regression and BERT as base models to train datasets sourced from DravidianLangTech@2025 for Tamil and Malayalam languages. The models were evaluated on test datasets, resulting in a 0.729 macro F1 score for BERT and 0.6279 for logistic regression in Tamil and Malayalam, respectively. |
| title | GS_DravidianLangTech@2025: Women Targeted Abusive Texts Detection on Social Media |
| topic | Computation and Language Social and Information Networks |
| url | https://arxiv.org/abs/2504.02863 |