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.

Misinformation detection in short videos on social media has become a pressing issue due to its popularity. However, datasets for misinformation detection are limited in terms of modality and sources, hindering the development of effective detection methods. In this article, we introduce a novel dataset denoted the multiplatform multimodal misinformation (3M) dataset. Our dataset is collected specifically to investigate and address misinformation in a multimodal context. A total of 17 352 videos were collected from two prominent social media platforms, namely TikTok and Weibo. The 3M dataset covers 30 different topics, such as sports, health, news, and art, providing a diverse range of content for analysis. We propose a novel approach named cross-modal attention misinformation detection (CAMD) for effectively detecting and addressing multimodal misinformation. CAMD leverages the cross-modal attention module to facilitate effective information exchange and fusion between modalities by learning the correlations and weights among them. The cross-modal attention module is capable of learning multilevel modality correlations, focuses primarily on the interaction between multimodal sequences across different time steps, and simultaneously adjusts the information from the source modality based on the information of the target modality. Extensive experiments on the 3M dataset show that the proposed method achieves state-of- the-art performance. Specifically, CAMD achieves accuracy, F1-score, precision, and recall values of 76.86%, 58.05%, 87.86%, and 58.70%, respectively, on the 3M dataset.

Social media serves as a real-time collecting and disseminating center of users’ ideas, opinions, and experiences. The deliberate disinformation and rumors propagate rapidly online due to their exaggerated facts, controversial opinions, divisive perspectives, and stunning expressions. Rumor detection approaches typically use social media posts with rumor or non-rumor labels for training and testing without disclosing the rationale behind decision-makings. On one hand, collecting evidence data to verify claims relies on expert efforts. On the other hand, verifying the truthfulness of confusing claims with distracting and lengthy evidences is still challenging. In this paper, we contribute a rumor detection dataset with multi-granularity evidences, denoted as the RD-E dataset, which includes response, fact-check, article, sourcing data and generated evidence by large language models, supporting models to verify the truthfulness of claims on social media. A number of 32,892 claims from 4,525 public individuals and organizations are annotated to 6 kinds of labels, including true, mostly true, half true, mostly false, false, pants on fire, covering a wide range of topics, e.g., politics, economy, society, technology, and health. In the experiments, seven rumor detection models have been investigated and customized on four predefined subtasks for comparisons.