Code for CIKM Short Paper "Supervised Contrastive Learning for Multimodal Unreliable News Detection in COVID-19 Pandemic"

The code for CIKM short paper "Supervised Contrastive Learning for Multimodal Unreliable News Detection in COVID-19 Pandemic". In this work, 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.