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Social media has become a popular means for people to consume news.Meanwhile, it also enables the wide dissemination of fake news, i.e., news withintentionally false information, which brings significant negative effects tothe society. Thus, fake news detection is attracting increasing attention.However, fake news detection is a non-trivial task, which requires multi-sourceinformation such as news content, social context, and dynamic information.First, fake news is written to fool people, which makes it difficult to detectfake news simply based on news contents. In addition to news contents, we needto explore social contexts such as user engagements and social behaviors. Forexample, a credible user's comment that "this is a fake news" is a strongsignal for detecting fake news. Second, dynamic information such as how fakenews and true news propagate and how users' opinions toward news pieces arevery important for extracting useful patterns for (early) fake news detectionand intervention. Thus, comprehensive datasets which contain news content,social context, and dynamic information could facilitate fake news propagation,detection, and mitigation; while to the best of our knowledge, existingdatasets only contains one or two aspects. Therefore, in this paper, tofacilitate fake news related researches, we provide a fake news data repositoryFakeNewsNet, which contains two comprehensive datasets that includes newscontent, social context, and dynamic information. We present a comprehensivedescription of datasets collection, demonstrate an exploratory analysis of thisdata repository from different perspectives, and discuss the benefits ofFakeNewsNet for potential applications on fake news study on social media.

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