Chinese COVID-19 Fake News Dataset

This dataset contains metadata about all COVID-related YouTube videos which circulated on public social media, but which YouTube eventually removed because they contained false information. It describes 8,122 videos that were shared between November 2019 and June 2020.

Data from 2002-, Berkman Klein Center
Lumen collects and studies online content removal requests, providing transparency and supporting analysis of the Web’s takedown “ecology,” in terms of who sends requests, why, and to what ends. Lumen seeks to facilitate research about different kinds of complaints and requests for removal – legitimate and questionable – that are being sent to Internet publishers, platforms, and service providers and, ultimately, to educate the public about the dynamics of this aspect of online participatory culture.

Empirical Studies of Conflict, Princeton University, 2020
Stories on misinformation efforts on social media and news outlets with sources, keywords, and direct links

Statista, 2022
Subscription source for MIT community use.

Computer, July 2021
The recent digitalization of news production, distribution, and consumption and the massive adoption and uses of social media have significantly contributed to an alarming case of misleading and unreliable information. Adding to that, the COVID-19 pandemic has not only brought fear but also new types of misinformation as people have started to turn to digital platforms for information about the virus.

Big Data, June 1, 2020
Despite several existing computational solutions on the detection of fake news, the lack of comprehensive and community-driven fake news data sets has become one of major roadblocks. Not only existing data sets are scarce, they do not contain a myriad of features often required in the study such as news content, social context, and spatiotemporal information. Therefore, in this article, to facilitate fake news-related research, we present a fake news data repository FakeNewsNet, which contains two comprehensive data sets with diverse features in news content, social context, and spatiotemporal information.

Data visualization has great potential for communicating complexity with clarity, but this requires iteration, empathy and tailoring to specific data and domains.