@inproceedings{thapa-etal-2025-multimodal,
title = "Multimodal Hate, Humor, and Stance Event Detection in Marginalized Sociopolitical Movements",
author = "Thapa, Surendrabikram and
Shah, Siddhant Bikram and
Rauniyar, Kritesh and
Shiwakoti, Shuvam and
Adhikari, Surabhi and
Veeramani, Hariram and
Johnson, Kristina T. and
Hurriyetoglu, Ali and
Tanev, Hristo and
Naseem, Usman",
editor = {H{\"u}rriyeto{\u{g}}lu, Ali and
Tanev, Hristo and
Thapa, Surendrabikram},
booktitle = "Proceedings of the 8th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Texts",
month = sep,
year = "2025",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2025.case-1.3/",
pages = "20--31",
abstract = "This paper presents the Shared Task on Multimodal Detection of Hate Speech, Humor, and Stance in Marginalized Socio-Political Movement Discourse, hosted at CASE 2025. The task is built on the PrideMM dataset, a curated collection of 5,063 text-embedded images related to the LGBTQ+ pride movement, annotated for four interrelated subtasks: (A) Hate Speech Detection, (B) Hate Target Classification, (C) Topical Stance Classification, and (D) Intended Humor Detection. Eighty-nine teams registered, with competitive submissions across all subtasks. The results show that multimodal approaches consistently outperform unimodal baselines, particularly for hate speech detection, while fine-grained tasks such as target identification and stance classification remain challenging due to label imbalance, multimodal ambiguity, and implicit or culturally specific content. CLIP-based models and parameter-efficient fusion architectures achieved strong performance, showing promising directions for low-resource and efficient multimodal systems."
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<abstract>This paper presents the Shared Task on Multimodal Detection of Hate Speech, Humor, and Stance in Marginalized Socio-Political Movement Discourse, hosted at CASE 2025. The task is built on the PrideMM dataset, a curated collection of 5,063 text-embedded images related to the LGBTQ+ pride movement, annotated for four interrelated subtasks: (A) Hate Speech Detection, (B) Hate Target Classification, (C) Topical Stance Classification, and (D) Intended Humor Detection. Eighty-nine teams registered, with competitive submissions across all subtasks. The results show that multimodal approaches consistently outperform unimodal baselines, particularly for hate speech detection, while fine-grained tasks such as target identification and stance classification remain challenging due to label imbalance, multimodal ambiguity, and implicit or culturally specific content. CLIP-based models and parameter-efficient fusion architectures achieved strong performance, showing promising directions for low-resource and efficient multimodal systems.</abstract>
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%0 Conference Proceedings
%T Multimodal Hate, Humor, and Stance Event Detection in Marginalized Sociopolitical Movements
%A Thapa, Surendrabikram
%A Shah, Siddhant Bikram
%A Rauniyar, Kritesh
%A Shiwakoti, Shuvam
%A Adhikari, Surabhi
%A Veeramani, Hariram
%A Johnson, Kristina T.
%A Hurriyetoglu, Ali
%A Tanev, Hristo
%A Naseem, Usman
%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 thapa-etal-2025-multimodal
%X This paper presents the Shared Task on Multimodal Detection of Hate Speech, Humor, and Stance in Marginalized Socio-Political Movement Discourse, hosted at CASE 2025. The task is built on the PrideMM dataset, a curated collection of 5,063 text-embedded images related to the LGBTQ+ pride movement, annotated for four interrelated subtasks: (A) Hate Speech Detection, (B) Hate Target Classification, (C) Topical Stance Classification, and (D) Intended Humor Detection. Eighty-nine teams registered, with competitive submissions across all subtasks. The results show that multimodal approaches consistently outperform unimodal baselines, particularly for hate speech detection, while fine-grained tasks such as target identification and stance classification remain challenging due to label imbalance, multimodal ambiguity, and implicit or culturally specific content. CLIP-based models and parameter-efficient fusion architectures achieved strong performance, showing promising directions for low-resource and efficient multimodal systems.
%U https://aclanthology.org/2025.case-1.3/
%P 20-31
Markdown (Informal)
[Multimodal Hate, Humor, and Stance Event Detection in Marginalized Sociopolitical Movements](https://aclanthology.org/2025.case-1.3/) (Thapa et al., CASE 2025)
ACL
- Surendrabikram Thapa, Siddhant Bikram Shah, Kritesh Rauniyar, Shuvam Shiwakoti, Surabhi Adhikari, Hariram Veeramani, Kristina T. Johnson, Ali Hurriyetoglu, Hristo Tanev, and Usman Naseem. 2025. Multimodal Hate, Humor, and Stance Event Detection in Marginalized Sociopolitical Movements. In Proceedings of the 8th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Texts, pages 20–31, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.