# Drink Bleach or Do What Now? Covid-HeRA: A Study of Risk-Informed Health Decision Making in the Presence of COVID-19 Misinformation

arXiv: https://arxiv.org/abs/2010.08743 · HTML: https://mlsanity.com/paper/2010.08743/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [Exploring news intent and its application: A theory-driven approach](https://mlsanity.com/paper/2312.16490/) (2023)
- [Foveate, Attribute, and Rationalize: Towards Physically Safe and Trustworthy AI](https://mlsanity.com/paper/2212.09667/) (2022)
- [Theme-driven Keyphrase Extraction to Analyze Social Media Discourse](https://mlsanity.com/paper/2301.11508/) (2023)
- [Not cool, calm or collected: Using emotional language to detect COVID-19 misinformation](https://mlsanity.com/paper/2303.16777/) (2023)
- [A Large-Scale Comparative Study of Accurate COVID-19 Information versus Misinformation](https://mlsanity.com/paper/2304.04811/) (2023)

## Abstract (arXiv)

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.