Manually Classified Real and Fake News Articles

Abstract

News articles that are written with an intent to deliberately deceive or manipulate readers are inherently problematic. These so-called 'fake news' articles are believed to have contributed to election manipulation and even resulted in severe injury and death, by actions that they have triggered. Identifying intentionally deceptive and manipulative news article and alerting human readers is key to mitigating the damage that they can produce. The dataset presented in this paper includes manually identified and classified news stories that can be used for the training and testing of classification systems that identify legitimate versus fake and manipulative news stories.