Supervised Contrastive Learning for Multimodal Unreliable News Detection in COVID-19 Pandemic
This addresses fake news detection for public information dissemination, but it is incremental as it applies existing contrastive learning methods to a specific domain.
The paper tackles the problem of detecting unreliable news during the COVID-19 pandemic by proposing a BERT-based multimodal framework that uses contrastive learning to leverage textual and visual information from related articles. It reports outperforming competitive baselines on the ReCOVery dataset.
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.