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Multimodal content, such as mixing text with images, presents significantchallenges to rumor detection in social media. Existing multimodal rumordetection has focused on mixing tokens among spatial and sequential locationsfor unimodal representation or fusing clues of rumor veracity acrossmodalities. However, they suffer from less discriminative unimodalrepresentation and are vulnerable to intricate location dependencies in thetime-consuming fusion of spatial and sequential tokens. This work makes thefirst attempt at multimodal rumor detection in the frequency domain, whichefficiently transforms spatial features into the frequency spectrum and obtainshighly discriminative spectrum features for multimodal representation andfusion. A novel Frequency Spectrum Representation and fUsion network (FSRU)with dual contrastive learning reveals the frequency spectrum is more effectivefor multimodal representation and fusion, extracting the informative componentsfor rumor detection. FSRU involves three novel mechanisms: utilizing theFourier transform to convert features in the spatial domain to the frequencydomain, the unimodal spectrum compression, and the cross-modal spectrumco-selection module in the frequency domain. Substantial experiments show thatFSRU achieves satisfactory multimodal rumor detection performance.

Frequency Spectrum is More Effective for Multimodal Representation and Fusion: A Multimodal Spectrum Rumor Detector