FNR: A Similarity and Transformer-Based Approach to Detect Multi-Modal Fake News in Social Media
This addresses the issue of fake news dissemination on social media, which misleads users, but the approach is incremental as it builds on existing multi-modal detection methods.
The paper tackled the problem of detecting fake news in social media by analyzing multi-modal features from text and images, proposing the FNR method which achieved higher accuracies on two real datasets compared to previous works.
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.