Paper Details
Authors: Md. Sajid Alam Chowdhury, Mostak Mahmud Chowdhury, Anik Mahmud Shanto, Jidan Al Abrar, Hasan Murad.
Venue: Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages at NAACL 2025, pages 459-464, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico, 2025.
Status: Published
Abstract
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.
BibTeX
@inproceedings{chowdhury-etal-2025-fired,
title = {{F}ired{\_}from{\_}{NLP}@{D}ravidian{L}ang{T}ech 2025: A Multimodal Approach for Detecting Misogynistic Content in {T}amil and {M}alayalam Memes},
author = {Chowdhury, Md. Sajid Alam and Chowdhury, Mostak Mahmud and Shanto, Anik Mahmud and Abrar, Jidan Al and Murad, Hasan},
booktitle = {Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages},
pages = {459--464},
year = {2025},
publisher = {Association for Computational Linguistics},
doi = {10.18653/v1/2025.dravidianlangtech-1.81},
url = {https://aclanthology.org/2025.dravidianlangtech-1.81/}
}