Following the Trail of Fake News Spreaders in Social Media: A Deep Learning Model

Even though the Internet and social media are usually safe and enjoyable, communication through social media also bears risks. For more than ten years, there have been concerns regarding the manipulation of public opinion through the social Web. In particular, misinformation spreading has proven effective in influencing people, their beliefs and behaviors, from swaying opinions on elections to having direct consequences on health during the COVID-19 pandemic. Most techniques in the literature focus on identifying the individual pieces of misinformation or fake news based on a set of stylistic, content-derived features, user profiles or sharing statistics. Recently, those methods have been extended to identify spreaders. However, they are not enough to effectively detect either fake content or the users spreading it. In this context, this paper presents an initial proof of concept of a deep learning model for identifying fake news spreaders in social media, focusing not only on the characteristics of the shared content but also on user interactions and the resulting content propagation tree structures. Although preliminary, an experimental evaluation over COVID-related data showed promising results, significantly outperforming other alternatives in the literature.