  The availability and interactive nature of social media have made them the
primary source of news around the globe. The popularity of social media tempts
criminals to pursue their immoral intentions by producing and disseminating
fake news using seductive text and misleading images. Therefore, verifying
social media news and spotting fakes is crucial. This work aims to analyze
multi-modal features from texts and images in social media for detecting fake
news. We propose a Fake News Revealer (FNR) method that utilizes transform
learning to extract contextual and semantic features and contrastive loss to
determine the similarity between image and text. We applied FNR on two real
social media datasets. The results show the proposed method achieves higher
accuracies in detecting fake news compared to the previous works.
