  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.
