FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media

TLDR

Social media is widely used for news consumption and sharing, yet it also facilitates the rapid spread of fake news that harms society, and while research on fake news detection has grown, existing datasets are scarce and lack comprehensive features such as content, social context, and spatiotemporal information. The authors introduce FakeNewsNet, a repository providing two comprehensive datasets rich in news content, social context, and spatiotemporal features, to support fake news research. The repository is described in detail, and exploratory analyses of its two datasets are performed from multiple perspectives to illustrate its utility for fake news studies on social media. Exploratory analyses of the two datasets reveal insights that demonstrate the repository’s usefulness for various fake news research applications on social media.

Abstract

Social media has become a popular means for people to consume and share the news. At the same time, however, it has also enabled the wide dissemination of fake news, that is, news with intentionally false information, causing significant negative effects on society. To mitigate this problem, the research of fake news detection has recently received a lot of attention. 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. We present a comprehensive description of the FakeNewsNet, demonstrate an exploratory analysis of two data sets from different perspectives, and discuss the benefits of the FakeNewsNet for potential applications on fake news study on social media.