KCRL@DravidianLangTech 2025: Multi-Pooling Feature Fusion with XLM-RoBERTa for Malayalam Fake News Detection and Classification

AbstractThe rapid spread of misinformation on social media platforms necessitates robust detection mechanisms, particularly for languages with limited computational resources. This paper presents our system for the DravidianLangTech 2025 shared task on Fake News Detection in Malayalam YouTube comments, addressing both binary and multiclass classification challenges. We propose a Multi-Pooling Feature Fusion (MPFF) architecture that leverages [CLS] + Mean + Max pooling strategy with transformer models. Our system demonstrates strong performance across both tasks, achieving a macro-averaged F1 score of 0.874, ranking 6th in binary classification, and 0.628, securing 1st position in multiclass classification. Experimental results show that our MPFF approach with XLM-RoBERTa significantly outperforms traditional machine learning and deep learning baselines, particularly excelling in the more challenging multiclass scenario. These findings highlight the effectiveness of our methodology in capturing nuanced linguistic features for fake news detection in Malayalam, contributing to the advancement of automated verification systems for Dravidian languages.