LexiLogic@DravidianLangTech 2025: Detecting Misogynistic Memes and Abusive Tamil and Malayalam Text Targeting Women on Social Media

Résumé fourni par la source

Social media platforms have become a significant medium for communication and expression, but they are also plagued by misogynistic content targeting women.This study focuses on detecting misogyny in memes and abusive textual content in Tamil and Malayalam languages, which are underrepresented in natural language processing research.Leveraging advanced machine learning and deep learning techniques, we developed a system capable of identifying misogynistic memes and abusive text.By addressing cultural and linguistic nuances, our approach enhances detection accuracy and contributes to safer online spaces for women.This work also serves as a foundation for expanding misogyny detection to other lowresource languages, fostering inclusivity and combating online abuse effectively.This paper presents our work on detecting misogynistic memes and abusive Tamil and Malayalam text targeting women on social media platforms.Leveraging the pretrained models l3cube-pune/tamil-bert and l3cubepune/malayalam-bert, we explored various data cleaning and augmentation strategies to enhance detection performance.The models were fine-tuned on curated datasets and evaluated using accuracy, F1-score, precision, and recall.The results demonstrated significant improvements with our cleaning and augmentation techniques, yielding robust performance in detecting nuanced and culturally-specific abusive content.Our model achieved macro F1 scores of 77.83/78.24 on L3Cube-Bert-Tamil and 78.16/77.01 on L3Cube-Bert-Malayalam, ranking 3rd and 4th on the leaderboard.For the misogyny task, we obtained 83.58/82.94 on L3Cube-Bert-Malayalam and 73.16/73.8 on L3Cube-Bert-Tamil, placing 9th in both.These results highlight our model's effectiveness in low-resource language classification.

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