A Systematic Literature Mapping on Profile Trustworthiness in Fake News Spread

Fake news is a problem for society as some people trust the message and react accordingly, propagating the lie. The consequences vary from a simple laugh to a death sentence. Research has focused on distinguishing between fake and fact through machine learning techniques. Most literature reviews and mappings focus on techniques that identify the fake over the message content. In a complementary way, we investigate fake news detection approaches based on the senders’ behavior on social media. We present a conceptual framework that deep dives into different aspects of the main categories of persuader, clarifier, and gullible actors. We also consider automatic and manual profiles. The results provide an overview of existing works on profile trustworthiness in fake news spread and shed light on possible research directions.