Drink bleach or do what now? Covid-HeRA: A dataset for risk-informed health decision making in the presence of COVID19 misinformation

Arkin R Dharawat, Ismini Lourentzou, Alex Morales, ChengXiang Zhai

Keywords: risk-aware misinformation, health misinformation, dataset, text classification, fine-grained, severity score

TL;DR: Covid-HeRA, dataset for health risk assessment of COVID-19 social media posts, annotated on the severity of each misinformation story, how harmful believed messages can be and what signals can detect refuted claims

Abstract: Given the wide spread 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 NLP community. To combat potential harm of COVID19-related misinformation, we release Covid-HeRA, a dataset for health risk assessment of COVID-19-related social media posts. More specifically, we study the severity of each misinformation story, i.e, how harmful a message believed by the audience can be and what type of signals can be used to discover high malicious fake news and detect refuted claims. We present a detailed analysis, evaluate several simple and advanced classification models, and conclude with our experimental analysis that presents open challenges and future directions.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2010.08743/code)