SAME: Sentiment-Aware Multi-Modal Embedding for Detecting Fake News

How to effectively detect fake news and prevent its diffusion on social media has gained much attention in recent years. However, relatively little focus has been given on exploiting user comments left for posts and latent sentiments therein in detecting fake news. Inspired by the rich information available in user comments on social media, therefore, we investigate whether the latent sentiments hidden in user comments can potentially help distinguish fake news from reliable content. We incorporate users' latent sentiments into an end-to-end deep embedding framework for detecting fake news, named as SAME. First, we use multi-modal networks to deal with heterogeneous data modalities. Second, to learn semantically meaningful spaces per data source, we adopt an adversarial mechanism. Third, we define a novel regularization loss to bring embeddings of relevant pairs closer. Our comprehensive validation using two real-world datasets, PolitiFact and GossipCop, demonstrates the effectiveness of SAME in detecting fake news, significantly outperforming state-of-the-art methods.