Beyond Single-View Detection: A Dual-Space Reasoning Framework for Interpretable Harmful Meme Understanding

ACL ARR 2026 January Submission2164 Authors

Keywords: detection of harmful memes, multimodality, mixture of experts

Abstract: The identification of harmful memes extends beyond a mere classification task, encompassing challenges related to multi-perspective semantic comprehension and hierarchical reasoning. Prevailing approaches predominantly depend on modal alignment or black-box classifiers, which fail to capture implicit biases and lack interpretability. In this study, we propose BPDMoE-Hate, a novel framework grounded in dual-space mixture-of-experts, which innovatively conceptualizes harmful meme detection as an integrated process of “viewpoint decoupling and hierarchical fusion''. Our approach generates adversarial binary perspectives via Visual-Language Models (VLMs) and incorporates an adaptive viewpoint gating to facilitate viewpoint selection, thereby enabling the model to autonomously discern implicit semantic inclinations. Moreover, we propose the Hyperbolic-Euclidean space expert to effectively capture the hierarchical structural relationships and semantic correlations between multimodal and viewpoint features, thereby enabling interpretable reasoning at the geometric representation level. Empirical evaluations conducted on three mainstream datasets demonstrate that BPDMoE-Hate not only substantially surpasses existing methodologies in performance but also offers visual explanations for viewpoint selection and hierarchical structuring, thereby advancing the field of interpretable multimodal content analysis.

Paper Type: Long

Research Area: Multimodality and Language Grounding to Vision, Robotics and Beyond

Research Area Keywords: Multimodality and Language Grounding to Vision, Robotics and Beyond,NLP Applications

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 2164