ReCOVery: A Multimodal Repository for COVID-19 News Credibility Research

TL;DR

ReCOVery is a comprehensive multimodal dataset of COVID-19 news articles and social media spread, designed to support research on detecting fake news and assessing news credibility during the pandemic.

Contribution

The paper introduces ReCOVery, a new multimodal COVID-19 news dataset with credibility labels and social media spread data, enabling advanced research in misinformation detection.

Findings

Dataset includes 2,029 news articles and 140,820 tweets.

Provides multimodal data: textual, visual, temporal, network.

Baseline models for credibility prediction are established.

Abstract

First identified in Wuhan, China, in December 2019, the outbreak of COVID-19 has been declared as a global emergency in January, and a pandemic in March 2020 by the World Health Organization (WHO). Along with this pandemic, we are also experiencing an "infodemic" of information with low credibility such as fake news and conspiracies. In this work, we present ReCOVery, a repository designed and constructed to facilitate research on combating such information regarding COVID-19. We first broadly search and investigate ~2,000 news publishers, from which 60 are identified with extreme [high or low] levels of credibility. By inheriting the credibility of the media on which they were published, a total of 2,029 news articles on coronavirus, published from January to May 2020, are collected in the repository, along with 140,820 tweets that reveal how these news articles have spread on the Twitter social network. The repository provides multimodal information of news articles on coronavirus, including textual, visual, temporal, and network information. The way that news credibility is obtained allows a trade-off between dataset scalability and label accuracy. Extensive experiments are conducted to present data statistics and distributions, as well as to provide baseline performances for predicting news credibility so that future methods can be compared. Our repository is available at http://coronavirus-fakenews.com.