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            <namePart type="given">Katerina</namePart>
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    <abstract>Misogyny meme detection is identifying memes that are harmful or offensive to women. These memes can hide hate behind jokes or images, making them difficult to identify. It’s important to detect them for a safer and respectful internet for everyone. Our model proposed a multimodal method for misogyny meme detection in Chinese social media by combining both textual and visual aspects of memes. The training and evaluation data were part of a shared task on detecting misogynistic content. We used a pretrained ResNet-50 architecture to extract visual representations of the memes and processed the meme transcriptions with BERT. The model fused modality-specific representations with a feed-forward neural net for classification. The selected pretrained models were frozen to avoid overfitting and to enhance generalization across all classes, and only the final classifier was fine-tuned on labelled meme recollection. The model was trained and evaluated using test data to achieve a macro F1-score of 0.70345. As a result, we have validated lightweight combining approaches for multimodal fusion techniques on noisy social media and how they can be validated in the context of hostile meme detection tasks.</abstract>
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%0 Conference Proceedings
%T SSNCSE@LT-EDI-2025:Detecting Misogyny Memes using Pretrained Deep Learning models
%A K, Sreeja
%A B, Bharathi
%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 k-b-2025-ssncse-lt
%X Misogyny meme detection is identifying memes that are harmful or offensive to women. These memes can hide hate behind jokes or images, making them difficult to identify. It’s important to detect them for a safer and respectful internet for everyone. Our model proposed a multimodal method for misogyny meme detection in Chinese social media by combining both textual and visual aspects of memes. The training and evaluation data were part of a shared task on detecting misogynistic content. We used a pretrained ResNet-50 architecture to extract visual representations of the memes and processed the meme transcriptions with BERT. The model fused modality-specific representations with a feed-forward neural net for classification. The selected pretrained models were frozen to avoid overfitting and to enhance generalization across all classes, and only the final classifier was fine-tuned on labelled meme recollection. The model was trained and evaluated using test data to achieve a macro F1-score of 0.70345. As a result, we have validated lightweight combining approaches for multimodal fusion techniques on noisy social media and how they can be validated in the context of hostile meme detection tasks.
%U https://aclanthology.org/2025.ltedi-1.1/
%P 1-5

Markdown (Informal)

[SSNCSE@LT-EDI-2025:Detecting Misogyny Memes using Pretrained Deep Learning models](https://aclanthology.org/2025.ltedi-1.1/) (K & B, LTEDI 2025)

• SSNCSE@LT-EDI-2025:Detecting Misogyny Memes using Pretrained Deep Learning models (K & B, LTEDI 2025)

ACL

• Sreeja K and Bharathi B. 2025. SSNCSE@LT-EDI-2025:Detecting Misogyny Memes using Pretrained Deep Learning models. In Proceedings of the 5th Conference on Language, Data and Knowledge: Fifth Workshop on Language Technology for Equality, Diversity, Inclusion, pages 1–5, Naples, Italy. Unior Press.