@inproceedings{preeti-etal-2025-herwill,
title = "{H}er{WILL}@{D}ravidian{L}ang{T}ech 2025: Ensemble Approach for Misogyny Detection in Memes Using Pre-trained Text and Vision Transformers",
author = "Preeti, Neelima Monjusha and
Chakraborty, Trina and
Arnob, Noor Mairukh Khan and
Mahmud, Saiyara and
Wasi, Azmine Toushik",
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-1.63/",
doi = "10.18653/v1/2025.dravidianlangtech-1.63",
pages = "355--360",
ISBN = "979-8-89176-228-2",
abstract = "Misogynistic memes on social media perpetuate gender stereotypes, contribute to harassment, and suppress feminist activism. However, most existing misogyny detection models focus on high-resource languages, leaving a gap in low-resource settings. This work addresses that gap by focusing on misogynistic memes in Tamil and Malayalam, two Dravidian languages with limited resources. We combine computer vision and natural language processing for multi-modal detection, using CLIP embeddings for the vision component and BERT models trained on code-mixed hate speech datasets for the text component. Our results show that this integrated approach effectively captures the unique characteristics of misogynistic memes in these languages, achieving competitive performance with a Macro F1 Score of 0.7800 for the Tamil test set and 0.8748 for the Malayalam test set. These findings highlight the potential of multimodal models and the adaptation of pre-trained models to specific linguistic and cultural contexts, advancing misogyny detection in low-resource settings. Code available at https://github.com/HerWILL-Inc/NAACL-2025"
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<abstract>Misogynistic memes on social media perpetuate gender stereotypes, contribute to harassment, and suppress feminist activism. However, most existing misogyny detection models focus on high-resource languages, leaving a gap in low-resource settings. This work addresses that gap by focusing on misogynistic memes in Tamil and Malayalam, two Dravidian languages with limited resources. We combine computer vision and natural language processing for multi-modal detection, using CLIP embeddings for the vision component and BERT models trained on code-mixed hate speech datasets for the text component. Our results show that this integrated approach effectively captures the unique characteristics of misogynistic memes in these languages, achieving competitive performance with a Macro F1 Score of 0.7800 for the Tamil test set and 0.8748 for the Malayalam test set. These findings highlight the potential of multimodal models and the adaptation of pre-trained models to specific linguistic and cultural contexts, advancing misogyny detection in low-resource settings. Code available at https://github.com/HerWILL-Inc/NAACL-2025</abstract>
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%0 Conference Proceedings
%T HerWILL@DravidianLangTech 2025: Ensemble Approach for Misogyny Detection in Memes Using Pre-trained Text and Vision Transformers
%A Preeti, Neelima Monjusha
%A Chakraborty, Trina
%A Arnob, Noor Mairukh Khan
%A Mahmud, Saiyara
%A Wasi, Azmine Toushik
%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 preeti-etal-2025-herwill
%X Misogynistic memes on social media perpetuate gender stereotypes, contribute to harassment, and suppress feminist activism. However, most existing misogyny detection models focus on high-resource languages, leaving a gap in low-resource settings. This work addresses that gap by focusing on misogynistic memes in Tamil and Malayalam, two Dravidian languages with limited resources. We combine computer vision and natural language processing for multi-modal detection, using CLIP embeddings for the vision component and BERT models trained on code-mixed hate speech datasets for the text component. Our results show that this integrated approach effectively captures the unique characteristics of misogynistic memes in these languages, achieving competitive performance with a Macro F1 Score of 0.7800 for the Tamil test set and 0.8748 for the Malayalam test set. These findings highlight the potential of multimodal models and the adaptation of pre-trained models to specific linguistic and cultural contexts, advancing misogyny detection in low-resource settings. Code available at https://github.com/HerWILL-Inc/NAACL-2025
%R 10.18653/v1/2025.dravidianlangtech-1.63
%U https://aclanthology.org/2025.dravidianlangtech-1.63/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.63
%P 355-360
Markdown (Informal)
[HerWILL@DravidianLangTech 2025: Ensemble Approach for Misogyny Detection in Memes Using Pre-trained Text and Vision Transformers](https://aclanthology.org/2025.dravidianlangtech-1.63/) (Preeti et al., DravidianLangTech 2025)
ACL
- Neelima Monjusha Preeti, Trina Chakraborty, Noor Mairukh Khan Arnob, Saiyara Mahmud, and Azmine Toushik Wasi. 2025. HerWILL@DravidianLangTech 2025: Ensemble Approach for Misogyny Detection in Memes Using Pre-trained Text and Vision Transformers. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 355–360, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.