Multimodal fake news detection (FND) increasingly demands verdicts that are not only accurate but traceable, revealing how cross-modal evidence is combined, yet two coupled difficulties remain. First, text-image relations are heterogeneous: uniqueness, redundancy, and synergy coexist and vary from post to post, so a single global fusion rule is brittle and opaque. Second, the dominant modality shifts across instances, which static encoders and a fixed fusion pathway handle poorly. We present IMEX-FND, an interaction-aware mixture-of-experts framework that couples adaptive routing with explicit interaction decomposition. A Multi-Modal Expert Gateway (MMEG) performs instance-wise, within-modality routing over specialized and shared experts and builds a CLIP-grounded cross-modal stream, yielding three refined, interaction-ready representations that adapt to the dominant modality of each post. An Interaction-aware Multi-Modal Expert Fusion (IMEF) module then decomposes the interactions among these streams into uniqueness, redundancy, and synergy, producing transparent, sample-wise weights via a modality-replacement training signal. The two stages form a single route-then-decompose pipeline whose routing and interaction weights are both inspectable, offering instance- and dataset-level traceability for misinformation diagnosis. Extensive experiments on the Weibo, Weibo-21, and Gossip benchmarks show that IMEX-FND achieves state-of-the-art performance, surpassing competitive baselines by 0.4-1.2% while offering superior traceability with fewer parameters.

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