  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.
