  In recent years, with the expansion of the Internet and attractive social
media infrastructures, people prefer to follow the news through these media.
Despite the many advantages of these media in the news field, the lack of any
control and verification mechanism has led to the spread of fake news, as one
of the most important threats to democracy, economy, journalism and freedom of
expression. Designing and using automatic methods to detect fake news on social
media has become a significant challenge. In this paper, we examine the
publishers' role in detecting fake news on social media. We also suggest a high
accurate multi-modal framework, namely FR-Detect, using user-related and
content-related features with early detection capability. For this purpose, two
new user-related features, namely Activity Credibility and Influence, have been
introduced for publishers. Furthermore, a sentence-level convolutional neural
network is provided to combine these features with latent textual content
features properly. Experimental results have shown that the publishers'
features can improve the performance of content-based models by up to 13% and
29% in accuracy and F1-score, respectively.
