Epidemiology inspired framework for fake news mitigation in social networks

Research in fake news detection and prevention has gained a lot of attention over the past decade, with most models using features generated from content and propagation paths. Complementary to these approaches, in this position paper we outline a framework inspired from the domain of epidemiology that proposes to identify people who are likely to become fake news spreaders. The proposed framework can serve as motivation to build fake news mitigation models, even for the scenario when fake news has not yet originated. Some models based on the framework have been successfully evaluated on real world Twitter datasets and can provide motivation for new research directions.