Classifying Real and Bot Users Based on their News Spread for Combating Misinformation

Online social media (OSM) have become the primary global news source, but because of the distributed nature of the web, it has increased the risk of misinformation spread. Fake news is misinformation that masquerades as genuine (real) information. Consequently, it is an active topic for researchers and OSM companies to find ways to identify and flag fake news, as well as detect the responsible sources that generate them. Bots, artificial users designed for various purposes, contribute to the dissemination of information on OSM. Regrettably, bots exhibit faster propagation of information compared to real users, often leading to the spread of inaccurate and low-quality information [1]. Differentiating between bots and real users solely based only on content poses a formidable task. This paper explores and expands techniques for bot detection based on news content as well as the spread diffusion process dynamics as a countermeasure for misinformation.