Fake News vs Satire: A Dataset and Analysis.

Fake news has become a major societal issue and a technical chal- lenge for social media companies to identify. This content is dif- cult to identify because the term "fake news" covers intention- ally false, deceptive stories as well as factual errors, satire, and sometimes, stories that a person just does not like. Addressing the problem requires clear de nitions and examples. In this work, we present a dataset of fake news and satire stories that are hand coded, veri ed, and, in the case of fake news, include rebutting stories. We also include a thematic content analysis of the articles, identifying major themes that include hyperbolic support or con- demnation of a gure, conspiracy theories, racist themes, and dis- crediting of reliable sources. In addition to releasing this dataset for research use, we analyze it and show results based on language that are promising for classi cation purposes. Overall, our contri- bution of a dataset and initial analysis are designed to support fu- ture work by fake news researchers.