DLRG@DravidianLangTech 2026: Explainable Transformer-Based Detection of Abusive Tamil Text Targeting Women on Social Media
Year: 2026
Type: conference-paper
Abstract: Many social media platforms have users who have normalized the abuse of women online, creating a need for systems that automatically detect such activity.For low-resource, regional languages like Tamil, which has informal writing styles, spelling variations, dialectal differences, and culturally specific expressions, it becomes a challenge to correctly detect abusive comments.In this work, a transformer-based approach for binary classification of Tamil comments into abusive and non-abusive categories is done using the DravidianLangTech dataset.The proposed system fine-tunes MuRIL(a multilingual transformer pretrained for Indian languages), enabling effective contextual representation with minimal preprocessing.To improve the transparency of the system, a post-hoc Explainable AI component is incorporated.A perturbation-based method using log-odds differences identifies words that significantly influence the predictions.Experimental findings indicate that the model reaches a validation accuracy exceeding 81% while also exhibiting a strong macro-F1 score.This research shows that utilizing contextual multilingual representations alongside simple interpretability methods offers a viable and effective approach for detecting abusive text in Tamil.The implementation of our system is publicly available at https://github.com/mirud5173/Abusive-Tamil- Comment-
Authors: Mirudhula Sankar, Ratnavel Rajalakshmi
Institution: Vellore Institute of Technology University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166843269