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In recent years, with the expansion of the Internet and attractive socialmedia infrastructures, people prefer to follow the news through these media.Despite the many advantages of these media in the news field, the lack of anycontrol and verification mechanism has led to the spread of fake news, as oneof the most important threats to democracy, economy, journalism and freedom ofexpression. Designing and using automatic methods to detect fake news on socialmedia has become a significant challenge. In this paper, we examine thepublishers' role in detecting fake news on social media. We also suggest a highaccurate multi-modal framework, namely FR-Detect, using user-related andcontent-related features with early detection capability. For this purpose, twonew user-related features, namely Activity Credibility and Influence, have beenintroduced for publishers. Furthermore, a sentence-level convolutional neuralnetwork is provided to combine these features with latent textual contentfeatures properly. Experimental results have shown that the publishers'features can improve the performance of content-based models by up to 13% and29% in accuracy and F1-score, respectively.

FR-Detect: A Multi-Modal Framework for Early Fake News Detection on Social Media Using Publishers Features