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.

The availability of datasets annotated with verified events by the public is a necessary prerequisite for unleashing the potential of multimodal deep learning for news event detection. Publicly available datasets are either incompletely annotated due to expensive cost, or ignore the verifiability of event labels, which are susceptible to bias and errors introduced by a limited number of annotators. In this article, we provide a YouTube dataset labelled by real-world news events that can be verified by Wikipedia-like crowd sourcing platforms, with the target of advancing temporal event detection. The events in our dataset cover a wide range of event topics including public security, natural disasters, elections, sports, and entertainment events, etc. In the dataset, each sample is labelled with real-world event that is verifiable by the public. We extensively evaluate the performance of 13 state-of-the-art algorithms on our dataset in a temporal manner, involving the multiple relationships between training and testing event labels, and provide a thorough analysis of the findings.

In this paper, we present a new dataset with the target of advancing temporal real-world event detection from video-sharing social media. The collected temporal event dataset (TED) has certain characteristics. 1) Certainty of event labels that can be recognized as real-world happening. 2) Wide range of event categories covering public security, natural disasters, elections, sports and entertainment events, etc. 3) High overlap among event topics ensuring the difficulties in distinguishing the labels. 4) Multiple data modalities involving textual, acoustic, and visual information, etc. More specifically, two scenarios are defined based on the close or open domains, i.e., temporal event detection without/with new events, denoted as TED-W and TED-N, respectively. For comparisons, a few benchmarks are investigated on the two scenarios with the dataset.