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    <abstract>Memes on social media have emerged as a crucial medium for disseminating vaccine-related viewpoints, yet their inherent irony, metaphor, and text-image misalignment pose significant challenges to automatic detection. In this paper, we propose MoEs-VaxAgent, a two-stage multimodal framework for vaccine critical meme detection. First, we design a dynamic routing Mixture-of-Experts module capable of adaptively capturing multi-granular semantic cues within memes. Second, to address hard samples located at the decision boundaries, we introduce an uncertainty-aware multi-agent rectification mechanism to perform a secondary detection on samples identified with low confidence in the first stage. In the EEUCA 2026 Shared Task on Multimodal Vaccine Critical Meme Detection, our system achieved a Macro F1-score of 0.8205, ranking 9th on the official leaderboard. Furthermore, we discuss various exploratory strategies evaluated during the competition and provide a detailed analysis of the model’s performance.</abstract>
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%0 Conference Proceedings
%T wenbin@EEUCA 2026: MoEs-VaxAgent, A Two-Stage Framework for Multimodal Vaccine Critical Meme Detection
%A Shen, Wenbin
%Y Hürriyetoğlu, Ali
%Y Thapa, Surendrabikram
%Y Tanev, Hristo
%S Proceedings of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-402-6
%F shen-2026-wenbin
%X Memes on social media have emerged as a crucial medium for disseminating vaccine-related viewpoints, yet their inherent irony, metaphor, and text-image misalignment pose significant challenges to automatic detection. In this paper, we propose MoEs-VaxAgent, a two-stage multimodal framework for vaccine critical meme detection. First, we design a dynamic routing Mixture-of-Experts module capable of adaptively capturing multi-granular semantic cues within memes. Second, to address hard samples located at the decision boundaries, we introduce an uncertainty-aware multi-agent rectification mechanism to perform a secondary detection on samples identified with low confidence in the first stage. In the EEUCA 2026 Shared Task on Multimodal Vaccine Critical Meme Detection, our system achieved a Macro F1-score of 0.8205, ranking 9th on the official leaderboard. Furthermore, we discuss various exploratory strategies evaluated during the competition and provide a detailed analysis of the model’s performance.
%U https://aclanthology.org/2026.eeuca-1.15/
%P 141-150

Markdown (Informal)

[wenbin@EEUCA 2026: MoEs-VaxAgent, A Two-Stage Framework for Multimodal Vaccine Critical Meme Detection](https://aclanthology.org/2026.eeuca-1.15/) (Shen, EEUCA 2026)

• wenbin@EEUCA 2026: MoEs-VaxAgent, A Two-Stage Framework for Multimodal Vaccine Critical Meme Detection (Shen, EEUCA 2026)

ACL

• Wenbin Shen. 2026. wenbin@EEUCA 2026: MoEs-VaxAgent, A Two-Stage Framework for Multimodal Vaccine Critical Meme Detection. In Proceedings of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA 2026), pages 141–150, San Diego, California, USA. Association for Computational Linguistics.