# GS_DravidianLangTech@2025: Women Targeted Abusive Texts Detection on Social Media

arXiv: https://arxiv.org/abs/2504.02863 · HTML: https://mlsanity.com/paper/2504.02863/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 31 references that are in the map.
- First posted on arXiv 1 Apr 2025.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Analyzing Emotional Trends from X platform using SenticNet: A Comparative Analysis with Cryptocurrency Price](https://mlsanity.com/paper/2405.03084/) (2024)
- [Comparative Approaches to Sentiment Analysis Using Datasets in Major European and Arabic Languages](https://mlsanity.com/paper/2501.12540/) (2025)
- [Identifying Different Layers of Online Misogyny](https://mlsanity.com/paper/2212.00480/) (2022)
- [Online Social Support Detection in Spanish Social Media Texts](https://mlsanity.com/paper/2502.09640/) (2025)

## Abstract (arXiv)

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.