Fired_from_NLP@DravidianLangTech 2025: A Multimodal Approach for Detecting Misogynistic Content in Tamil and Malayalam Memes
Published:
In the context of online platforms, identifying misogynistic content in memes is crucial for maintaining a safe and respectful environment. While most research has focused on high-resource languages, there is limited work on languages like Tamil and Malayalam. To address this gap, we have participated in the Misogyny Meme Detection task organized by DravidianLangTech@NAACL 2025, utilizing the provided dataset named MDMD (Misogyny Detection Meme Dataset), which consists of Tamil and Malayalam memes. In this paper, we have proposed a multimodal approach combining visual and textual features to detect misogynistic content. Through a comparative analysis of different model configurations, combining various deep learning-based CNN architectures and transformer-based models, we have developed fine-tuned multimodal models that effectively identify misogynistic memes in Tamil and Malayalam. We have achieved an F1 score of 0.678 for Tamil memes and 0.803 for Malayalam memes.
Authors: Md. Sajid Alam Chowdhury, Mostak Chowdhury, Anik Shanto, and Hasan Murad.
Paper URL: https://aclanthology.org/2025.dravidianlangtech-1.81/