@inproceedings{chakraborty-etal-2025-one-zero-dravidianlangtech,
title = "One{\_}by{\_}zero@{D}ravidian{L}ang{T}ech 2025: A Multimodal Approach for Misogyny Meme Detection in {M}alayalam Leveraging Visual and Textual Features",
author = "Chakraborty, Dola and
Afroz, Shamima and
Hossain, Jawad and
Hoque, Mohammed Moshiul",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth and
Rajiakodi, Saranya and
Palani, Balasubramanian and
Subramanian, Malliga and
Cn, Subalalitha and
Chinnappa, Dhivya",
booktitle = "Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
month = may,
year = "2025",
address = "Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.dravidianlangtech-1.89/",
doi = "10.18653/v1/2025.dravidianlangtech-1.89",
pages = "508--513",
ISBN = "979-8-89176-228-2",
abstract = "Misogyny memes are a form of online content that spreads harmful and damaging ideas about women. By combining images and text, they often aim to mock, disrespect, or insult women, sometimes overtly and other times in more subtle, insidious ways. Detecting Misogyny memes is crucial for fostering safer and more respectful online communities. While extensive research has been conducted on high-resource languages (HRLs) like English, low-resource languages (LRLs) such as Dravidian (e.g., Tamil and Malayalam) remain largely overlooked. The shared task on Misogyny Meme Detection, organized as part of DravidianLangTech@NAACL 2025, provided a platform to tackle the challenge of identifying misogynistic content in memes, specifically in Malayalam. We participated in the competition and adopted a multimodal approach to contribute to this effort. For image analysis, we employed a ResNet18 model to extract visual features, while for text analysis, we utilized the IndicBERT model. Our system achieved an impressive F1-score of 0.87, earning us the 3rd rank in the task."
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%0 Conference Proceedings
%T One_by_zero@DravidianLangTech 2025: A Multimodal Approach for Misogyny Meme Detection in Malayalam Leveraging Visual and Textual Features
%A Chakraborty, Dola
%A Afroz, Shamima
%A Hossain, Jawad
%A Hoque, Mohammed Moshiul
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%Y Rajiakodi, Saranya
%Y Palani, Balasubramanian
%Y Subramanian, Malliga
%Y Cn, Subalalitha
%Y Chinnappa, Dhivya
%S Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2025
%8 May
%I Association for Computational Linguistics
%C Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico
%@ 979-8-89176-228-2
%F chakraborty-etal-2025-one-zero-dravidianlangtech
%X Misogyny memes are a form of online content that spreads harmful and damaging ideas about women. By combining images and text, they often aim to mock, disrespect, or insult women, sometimes overtly and other times in more subtle, insidious ways. Detecting Misogyny memes is crucial for fostering safer and more respectful online communities. While extensive research has been conducted on high-resource languages (HRLs) like English, low-resource languages (LRLs) such as Dravidian (e.g., Tamil and Malayalam) remain largely overlooked. The shared task on Misogyny Meme Detection, organized as part of DravidianLangTech@NAACL 2025, provided a platform to tackle the challenge of identifying misogynistic content in memes, specifically in Malayalam. We participated in the competition and adopted a multimodal approach to contribute to this effort. For image analysis, we employed a ResNet18 model to extract visual features, while for text analysis, we utilized the IndicBERT model. Our system achieved an impressive F1-score of 0.87, earning us the 3rd rank in the task.
%R 10.18653/v1/2025.dravidianlangtech-1.89
%U https://aclanthology.org/2025.dravidianlangtech-1.89/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.89
%P 508-513
Markdown (Informal)
[One_by_zero@DravidianLangTech 2025: A Multimodal Approach for Misogyny Meme Detection in Malayalam Leveraging Visual and Textual Features](https://aclanthology.org/2025.dravidianlangtech-1.89/) (Chakraborty et al., DravidianLangTech 2025)
ACL
- Dola Chakraborty, Shamima Afroz, Jawad Hossain, and Mohammed Moshiul Hoque. 2025. One_by_zero@DravidianLangTech 2025: A Multimodal Approach for Misogyny Meme Detection in Malayalam Leveraging Visual and Textual Features. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 508–513, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.