@inproceedings{rahman-etal-2025-cuet-12033,
title = "{CUET}{\_}12033@{LT}-{EDI}-2025: Misogyny Detection",
author = "Rahman, Mehreen and
Fariha, Faozia and
Tabassum, Nabilah and
Rahman, Samia and
Murad, Hasan",
editor = "Gkirtzou, Katerina and
{\v{Z}}itnik, Slavko and
Gracia, Jorge and
Gromann, Dagmar and
di Buono, Maria Pia and
Monti, Johanna and
Ionov, Maxim",
booktitle = "Proceedings of the 5th Conference on Language, Data and Knowledge: Fifth Workshop on Language Technology for Equality, Diversity, Inclusion",
month = sep,
year = "2025",
address = "Naples, Italy",
publisher = "Unior Press",
url = "https://aclanthology.org/2025.ltedi-1.22/",
pages = "127--132",
ISBN = "978-88-6719-334-9",
abstract = "Misogynistic memes spread harmful stereotypes and toxic content across social media platforms, often combining sarcastic text and offensive visuals that make them difficult to detect using traditional methods. Our research has been part of the the Shared Task on Misogyny Meme Detection - LT- EDI@LDK 2025, identifying misogynistic memes using deep learning-based multimodal approach that leverages both textual and visual information for accurate classification of such memes. We experiment with various models including CharBERT, BiLSTM, and CLIP for text and image encoding, and explore fusion strategies like early and gated fusion. Our best performing model, CharBERT + BiLSTM + CLIP with gated fusion, achieves strong results, showing the effectiveness of combining features from both modalities. To address challenges like language mixing and class imbalance, we apply preprocessing techniques (e.g., Romanizing Chinese text) and data augmentation (e.g., image transformations, text back-translation). The results demonstrate significant improvements over unimodal baselines, highlighting the value of multimodal learning in detecting subtle and harmful content online."
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<abstract>Misogynistic memes spread harmful stereotypes and toxic content across social media platforms, often combining sarcastic text and offensive visuals that make them difficult to detect using traditional methods. Our research has been part of the the Shared Task on Misogyny Meme Detection - LT- EDI@LDK 2025, identifying misogynistic memes using deep learning-based multimodal approach that leverages both textual and visual information for accurate classification of such memes. We experiment with various models including CharBERT, BiLSTM, and CLIP for text and image encoding, and explore fusion strategies like early and gated fusion. Our best performing model, CharBERT + BiLSTM + CLIP with gated fusion, achieves strong results, showing the effectiveness of combining features from both modalities. To address challenges like language mixing and class imbalance, we apply preprocessing techniques (e.g., Romanizing Chinese text) and data augmentation (e.g., image transformations, text back-translation). The results demonstrate significant improvements over unimodal baselines, highlighting the value of multimodal learning in detecting subtle and harmful content online.</abstract>
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%0 Conference Proceedings
%T CUET_12033@LT-EDI-2025: Misogyny Detection
%A Rahman, Mehreen
%A Fariha, Faozia
%A Tabassum, Nabilah
%A Rahman, Samia
%A Murad, Hasan
%Y Gkirtzou, Katerina
%Y Žitnik, Slavko
%Y Gracia, Jorge
%Y Gromann, Dagmar
%Y di Buono, Maria Pia
%Y Monti, Johanna
%Y Ionov, Maxim
%S Proceedings of the 5th Conference on Language, Data and Knowledge: Fifth Workshop on Language Technology for Equality, Diversity, Inclusion
%D 2025
%8 September
%I Unior Press
%C Naples, Italy
%@ 978-88-6719-334-9
%F rahman-etal-2025-cuet-12033
%X Misogynistic memes spread harmful stereotypes and toxic content across social media platforms, often combining sarcastic text and offensive visuals that make them difficult to detect using traditional methods. Our research has been part of the the Shared Task on Misogyny Meme Detection - LT- EDI@LDK 2025, identifying misogynistic memes using deep learning-based multimodal approach that leverages both textual and visual information for accurate classification of such memes. We experiment with various models including CharBERT, BiLSTM, and CLIP for text and image encoding, and explore fusion strategies like early and gated fusion. Our best performing model, CharBERT + BiLSTM + CLIP with gated fusion, achieves strong results, showing the effectiveness of combining features from both modalities. To address challenges like language mixing and class imbalance, we apply preprocessing techniques (e.g., Romanizing Chinese text) and data augmentation (e.g., image transformations, text back-translation). The results demonstrate significant improvements over unimodal baselines, highlighting the value of multimodal learning in detecting subtle and harmful content online.
%U https://aclanthology.org/2025.ltedi-1.22/
%P 127-132
Markdown (Informal)
[CUET_12033@LT-EDI-2025: Misogyny Detection](https://aclanthology.org/2025.ltedi-1.22/) (Rahman et al., LTEDI 2025)
ACL
- Mehreen Rahman, Faozia Fariha, Nabilah Tabassum, Samia Rahman, and Hasan Murad. 2025. CUET_12033@LT-EDI-2025: Misogyny Detection. In Proceedings of the 5th Conference on Language, Data and Knowledge: Fifth Workshop on Language Technology for Equality, Diversity, Inclusion, pages 127–132, Naples, Italy. Unior Press.