  Multimodal content, such as mixing text with images, presents significant
challenges to rumor detection in social media. Existing multimodal rumor
detection has focused on mixing tokens among spatial and sequential locations
for unimodal representation or fusing clues of rumor veracity across
modalities. However, they suffer from less discriminative unimodal
representation and are vulnerable to intricate location dependencies in the
time-consuming fusion of spatial and sequential tokens. This work makes the
first attempt at multimodal rumor detection in the frequency domain, which
efficiently transforms spatial features into the frequency spectrum and obtains
highly discriminative spectrum features for multimodal representation and
fusion. A novel Frequency Spectrum Representation and fUsion network (FSRU)
with dual contrastive learning reveals the frequency spectrum is more effective
for multimodal representation and fusion, extracting the informative components
for rumor detection. FSRU involves three novel mechanisms: utilizing the
Fourier transform to convert features in the spatial domain to the frequency
domain, the unimodal spectrum compression, and the cross-modal spectrum
co-selection module in the frequency domain. Substantial experiments show that
FSRU achieves satisfactory multimodal rumor detection performance.
