Supervised Contrastive Learning for Multimodal Unreliable News Detection in COVID-19 Pandemic
As the digital news industry becomes the main channel of information dissemination, the adverse impact of fake news is explosively magnified. The credibility of a news report should not be considered in isolation. Rather, previously published news articles on the similar event could be used to assess the credibility of a news report. Inspired by this, we propose a BERT-based multimodal unreliable news detection framework, which captures both textual and visual information from unreliable articles utilising the contrastive learning strategy. The contrastive learner interacts with the unreliable news classifier to push similar credible news (or similar unreliable news) closer while moving news articles with similar content but opposite credibility labels away from each other in the multimodal embedding space. Experimental results on a COVID-19 related dataset, ReCOVery, show that our model outperforms a number of competitive baseline in unreliable news detection.
Code (1)
Tasks
ArticlesContrastive LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
External Reliable Information-enhanced Multimodal Contrastive Learning for Fake News Detection
With the rapid development of the Internet, the information dissemination paradigm has changed and the efficiency has been improved greatly. While this also brings the quick spread of fake news and leads to negative impa…
Contrastive LearningFake News DetectionCross-modal Contrastive Learning for Multimodal Fake News Detection
Automatic detection of multimodal fake news has gained a widespread attention recently. Many existing approaches seek to fuse unimodal features to produce multimodal news representations. However, the potential of powerf…
Contrastive LearningFake News DetectionOpen-Ended Question AnsweringMMCoVaR: Multimodal COVID-19 Vaccine Focused Data Repository for Fake News Detection and a Baseline Architecture for Classification
The outbreak of COVID-19 has resulted in an "infodemic" that has encouraged the propagation of misinformation about COVID-19 and cure methods which, in turn, could negatively affect the adoption of recommended public hea…
ArticlesFake News DetectionMisinformationStance DetectionIDO: Incongruity-aware Distribution Optimization for Multimodal Fake News Detection
Multimodal fake news detection aims to identify the authenticity of news. Existing multimodal fake news detection methods mainly focus on cross-modal consistency, but often fail to explicitly model the semantic incongrui…
Contrastive LearningFake News DetectionMultimodal Fake News Detection via CLIP-Guided Learning
Multimodal fake news detection has attracted many research interests in social forensics. Many existing approaches introduce tailored attention mechanisms to guide the fusion of unimodal features. However, how the simila…
Decision MakingFake News Detectionfeature selection