MKV: Mapping Key Semantics into Vectors for Rumor Detection

The cross-attention mechanism has been widely employed in the multimodal rumor detection task, which is computation-intensive and suffers from the restricted modal receptive field. In this paper, we propose a multimodal rumor detection model (MKV), which maps multimodal key semantics with discrimination into feature vectors for rumor detection. More specifically, MKV extracts high-dimensional features for each modality separately by the Multimodal Feature Extractor (MFE). The mapping mechanism learns low-dimensional mapping scheme (Map) and key semantics (Key) with discrimination from the different modal features respectively. Subsequently, the Map and Key jointly construct a state matrix (State) containing all possible permutations of modalities. In particular, a max pooling operation is performed on State and products a feature vector (Vector). The mapping mechanism is able to incrementally learn the discriminative semantics by stacking manner. Vectors from the stacking process are leveraged in the Rumor Detection module (RD). Extensive experiments on two public datasets show that the MKV achieves the state-of-the-art performance.