  Given the widespread dissemination of inaccurate medical advice related to
the 2019 coronavirus pandemic (COVID-19), such as fake remedies, treatments and
prevention suggestions, misinformation detection has emerged as an open problem
of high importance and interest for the research community. Several works study
health misinformation detection, yet little attention has been given to the
perceived severity of misinformation posts. In this work, we frame health
misinformation as a risk assessment task. More specifically, we study the
severity of each misinformation story and how readers perceive this severity,
i.e., how harmful a message believed by the audience can be and what type of
signals can be used to recognize potentially malicious fake news and detect
refuted claims. To address our research questions, we introduce a new benchmark
dataset, accompanied by detailed data analysis. We evaluate several traditional
and state-of-the-art models and show there is a significant gap in performance
when applying traditional misinformation classification models to this task. We
conclude with open challenges and future directions.
