@inproceedings{chakravarthi-etal-2025-findings,
title = "Findings of the Shared Task on Misogyny Meme Detection: {D}ravidian{L}ang{T}ech@{NAACL} 2025",
author = "Chakravarthi, Bharathi Raja and
Ponnusamy, Rahul and
Rajiakodi, Saranya and
Chinnan, Shunmuga Priya Muthusamy and
Buitelaar, Paul and
Sivagnanam, Bhuvaneswari and
Kizhakkeparambil, Anshid",
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-1.123/",
doi = "10.18653/v1/2025.dravidianlangtech-1.123",
pages = "721--731",
ISBN = "979-8-89176-228-2",
abstract = "The rapid expansion of social media has facilitated communication but also enabled the spread of misogynistic memes, reinforcing gender stereotypes and toxic online environments. Detecting such content is challenging due to the multimodal nature of memes, where meaning emerges from the interplay of text and images. The Misogyny Meme Detection shared task at DravidianLangTech@NAACL 2025 focused on Tamil and Malayalam, encouraging the development of multimodal approaches. With 114 teams registered and 23 submitting predictions, participants leveraged various pretrained language models and vision models through fusion techniques. The best models achieved high macro F1 scores (0.83682 for Tamil, 0.87631 for Malayalam), highlighting the effectiveness of multimodal learning. Despite these advances, challenges such as bias in the data set, class imbalance, and cultural variations persist. Future research should refine multimodal detection methods to improve accuracy and adaptability, fostering safer and more inclusive online spaces."
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%0 Conference Proceedings
%T Findings of the Shared Task on Misogyny Meme Detection: DravidianLangTech@NAACL 2025
%A Chakravarthi, Bharathi Raja
%A Ponnusamy, Rahul
%A Rajiakodi, Saranya
%A Chinnan, Shunmuga Priya Muthusamy
%A Buitelaar, Paul
%A Sivagnanam, Bhuvaneswari
%A Kizhakkeparambil, Anshid
%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 chakravarthi-etal-2025-findings
%X The rapid expansion of social media has facilitated communication but also enabled the spread of misogynistic memes, reinforcing gender stereotypes and toxic online environments. Detecting such content is challenging due to the multimodal nature of memes, where meaning emerges from the interplay of text and images. The Misogyny Meme Detection shared task at DravidianLangTech@NAACL 2025 focused on Tamil and Malayalam, encouraging the development of multimodal approaches. With 114 teams registered and 23 submitting predictions, participants leveraged various pretrained language models and vision models through fusion techniques. The best models achieved high macro F1 scores (0.83682 for Tamil, 0.87631 for Malayalam), highlighting the effectiveness of multimodal learning. Despite these advances, challenges such as bias in the data set, class imbalance, and cultural variations persist. Future research should refine multimodal detection methods to improve accuracy and adaptability, fostering safer and more inclusive online spaces.
%R 10.18653/v1/2025.dravidianlangtech-1.123
%U https://aclanthology.org/2025.dravidianlangtech-1.123/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.123
%P 721-731
Markdown (Informal)
[Findings of the Shared Task on Misogyny Meme Detection: DravidianLangTech@NAACL 2025](https://aclanthology.org/2025.dravidianlangtech-1.123/) (Chakravarthi et al., DravidianLangTech 2025)
ACL
- Bharathi Raja Chakravarthi, Rahul Ponnusamy, Saranya Rajiakodi, Shunmuga Priya Muthusamy Chinnan, Paul Buitelaar, Bhuvaneswari Sivagnanam, and Anshid Kizhakkeparambil. 2025. Findings of the Shared Task on Misogyny Meme Detection: DravidianLangTech@NAACL 2025. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 721–731, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.