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        <title>Team MemeMasters@CASE 2025: Adapting Vision-Language Models for Understanding Hate Speech in Multimodal Content</title>
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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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            <namePart type="given">Ali</namePart>
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    <abstract>Social media memes have become a powerful form of digital communication, combining images and text to convey humor, social commentary, and sometimes harmful content. This paper presents a multimodal approach using a fine-tuned CLIP model to analyze textembedded images in the CASE 2025 Shared Task. We address four subtasks: Hate Speech Detection, Target Classification, Stance Detection, and Humor Detection. Our method effectively captures visual and textual signals, achieving strong performance with precision of 80% for the detection of hate speech and 76% for the detection of humor, while stance and target classification achieved a precision of 60% and 54%, respectively. Detailed evaluations with classification reports and confusion matrices highlight the ability of the model to handle complex multimodal signals in social media content, demonstrating the potential of vision-language models for computational social science applications.</abstract>
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%0 Conference Proceedings
%T Team MemeMasters@CASE 2025: Adapting Vision-Language Models for Understanding Hate Speech in Multimodal Content
%A Gurung, Shruti
%A Shakya, Shubham
%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 gurung-shakya-2025-team
%X Social media memes have become a powerful form of digital communication, combining images and text to convey humor, social commentary, and sometimes harmful content. This paper presents a multimodal approach using a fine-tuned CLIP model to analyze textembedded images in the CASE 2025 Shared Task. We address four subtasks: Hate Speech Detection, Target Classification, Stance Detection, and Humor Detection. Our method effectively captures visual and textual signals, achieving strong performance with precision of 80% for the detection of hate speech and 76% for the detection of humor, while stance and target classification achieved a precision of 60% and 54%, respectively. Detailed evaluations with classification reports and confusion matrices highlight the ability of the model to handle complex multimodal signals in social media content, demonstrating the potential of vision-language models for computational social science applications.
%U https://aclanthology.org/2025.case-1.18/
%P 146-151

Markdown (Informal)

[Team MemeMasters@CASE 2025: Adapting Vision-Language Models for Understanding Hate Speech in Multimodal Content](https://aclanthology.org/2025.case-1.18/) (Gurung & Shakya, CASE 2025)

• Team MemeMasters@CASE 2025: Adapting Vision-Language Models for Understanding Hate Speech in Multimodal Content (Gurung & Shakya, CASE 2025)

ACL

• Shruti Gurung and Shubham Shakya. 2025. Team MemeMasters@CASE 2025: Adapting Vision-Language Models for Understanding Hate Speech in Multimodal Content. In Proceedings of the 8th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Texts, pages 146–151, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.