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