This paper introduces MIMOE-FND, a new model for detecting fake news using multiple sources like text and images, focusing on how these sources interact.
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Paper - https://arxiv.org/abs/2501.12431
Methods in this Paper 💡:
→ MIMOE-FND uses a hierarchical Mixture-of-Experts (MoE) architecture.
→ It categorizes interactions based on agreement between single-source predictions and semantic alignment.
→ It uses an interaction gating mechanism, routing input to specialized fusion experts based on interaction type.
→ The model has specific experts for each interaction type (Agreed Misalignment, Agreed Alignment, Disagreed Misalignment, and Disagreed Alignment).
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Key Insights from this Paper 🔑:
→ Modality interactions are crucial in multimodal fake news detection.
→ Tailoring fusion strategies to different interaction scenarios improves accuracy.
→ Joint supervision using both agreement and alignment is essential.
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Results 💯:
→ Achieves accuracy of 0.928 on Weibo, 0.895 on GossipCop, and 0.956 on Weibo-21.
→ Outperforms state-of-the-art methods across multiple datasets and languages.
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