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        <namePart type="given">Bidhan</namePart>
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            <title>Proceedings of the 8th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Texts</title>
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        <name type="personal">
            <namePart type="given">Ali</namePart>
            <namePart type="family">Hürriyetoğlu</namePart>
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    <abstract>Marginalized socio-political movements have become focal points of online discourse, polarizing public opinion and attracting attention through controversial or humorous content. Memes, play a powerful role in shaping this discourse both as tools of empowerment, and as vessels for ridicule or hate. The ambiguous and highly contextual nature of these memes presents a unique challenge for computational systems. In this work we try to identify these trends. Our approach leverages the BERT+ResNet(BERTRES) model to classify the multimodal content into different categories based on different tasks for the Shared Task on Multimodal Detection of Hate Speech, Humor, and Stance in Marginalized SocioPolitical Movement Discourse at CASE 2025. The task is divided into four sub-tasks: subtask A focuses on detection of hate speech, subtask B focuses on classifying the targets of hate speech, subtask C focuses on classification of topical stance and subtask D focuses on detection of intended humor. Our approach obtained a 0.73 F1 score in subtask A, 0.56 F1 score in subtask B, 0.6 F1 score in subtask C, 0.65 F1 score in subtask D.</abstract>
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%0 Conference Proceedings
%T Overfitters@CASE2025: Multimodal Hate Speech Analysis Using BERT and RESNET
%A Bhattarai, Bidhan Chandra
%A Pokhrel, Dipshan
%A Maharjan, Ishan
%A Thapa, Rabin
%Y Hürriyetoğlu, Ali
%Y Tanev, Hristo
%Y Thapa, Surendrabikram
%S Proceedings of the 8th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Texts
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F bhattarai-etal-2025-overfitters
%X Marginalized socio-political movements have become focal points of online discourse, polarizing public opinion and attracting attention through controversial or humorous content. Memes, play a powerful role in shaping this discourse both as tools of empowerment, and as vessels for ridicule or hate. The ambiguous and highly contextual nature of these memes presents a unique challenge for computational systems. In this work we try to identify these trends. Our approach leverages the BERT+ResNet(BERTRES) model to classify the multimodal content into different categories based on different tasks for the Shared Task on Multimodal Detection of Hate Speech, Humor, and Stance in Marginalized SocioPolitical Movement Discourse at CASE 2025. The task is divided into four sub-tasks: subtask A focuses on detection of hate speech, subtask B focuses on classifying the targets of hate speech, subtask C focuses on classification of topical stance and subtask D focuses on detection of intended humor. Our approach obtained a 0.73 F1 score in subtask A, 0.56 F1 score in subtask B, 0.6 F1 score in subtask C, 0.65 F1 score in subtask D.
%U https://aclanthology.org/2025.case-1.9/
%P 76-82

Markdown (Informal)

[Overfitters@CASE2025: Multimodal Hate Speech Analysis Using BERT and RESNET](https://aclanthology.org/2025.case-1.9/) (Bhattarai et al., CASE 2025)

• Overfitters@CASE2025: Multimodal Hate Speech Analysis Using BERT and RESNET (Bhattarai et al., CASE 2025)

ACL

• Bidhan Chandra Bhattarai, Dipshan Pokhrel, Ishan Maharjan, and Rabin Thapa. 2025. Overfitters@CASE2025: Multimodal Hate Speech Analysis Using BERT and RESNET. In Proceedings of the 8th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Texts, pages 76–82, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.