Misinformation in public health

Abstract

Following the tsunami of misinterpreted, manipulated and malicious information growing on the Internet, the misinformation surrounding COVID-19 has taken centre stage. In the context of the current COVID-19 pandemic, publications and social media platforms are particularly vulnerable to rumors and misinformation given the acute uncertainty surrounding the virus itself. At the same time, the uncertainty and new nature ofCOVID-19 means that what may appear to be a "rumor" - yet another piece of unverified information - may be an important indication of the behavior and impact of this new virus. We attempt to tackle this phenomenon by applying different Machine Learning models and Natural Language Processing techniques with a focus on Twitter and web articles. A thorough review of the data and its metrics has also been presented.