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Over the past couple of years, the topic of "fake news" and its influenceover people's opinions has become a growing cause for concern. Although thespread of disinformation on the Internet is not a new phenomenon, thewidespread use of social media has exacerbated its effects, providing morechannels for dissemination and the potential to "go viral." Nowhere was thismore evident than during the 2016 United States Presidential Election. Althoughthe current of disinformation spread via trolls, bots, and hyperpartisan mediaoutlets likely reinforced existing biases rather than sway undecided voters,the effects of this deluge of disinformation are by no means trivial. Theconsequences range in severity from an overall distrust in news media, to anill-informed citizenry, and in extreme cases, provocation of violent action. Itis clear that human ability to discern lies from truth is flawed at best. Assuch, greater attention has been given towards applying machine learningapproaches to detect deliberately deceptive news articles. This paper looks atthe work that has already been done in this area.

Disinformation Detection: A review of linguistic feature selection and classification models in news veracity assessments