FNR: A Similarity and Transformer-Based Approachto Detect Multi-Modal FakeNews in Social Media

• URL: http://arxiv.org/abs/2112.01131v1
• Date: Thu, 2 Dec 2021 11:12:09 GMT
• Title: FNR: A Similarity and Transformer-Based Approachto Detect Multi-Modal FakeNews in Social Media
• Authors: Faeze Ghorbanpour, Maryam Ramezani, Mohammad A. Fazli and Hamid R. Rabiee
• Abstract summary: 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. The results show the proposed method achieves higher accuracies in detecting fake news compared to the previous works.
• Score: 4.607964446694258
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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