wenbin@EEUCA 2026: MoEs-VaxAgent, A Two-Stage Framework for Multimodal Vaccine Critical Meme Detection
Year: 2026
Type: conference-paper
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.
Author: Wenbin Shen
Institution: Nanjing University of Science and Technology
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7166808926