The recent controversy over ‘fake news' reminds us of one of the main problems on the web today: the utilization of social media and other outlets to distribute false and misleading content. The impact of this problem is very significant. This article discusses the issue of fake content on the web. First, it defines the problem and shows that, in many cases, it is surprisingly hard to establish when a piece of news is untrue. It distinguishes the issue of fake content from issues of hate/offensive speech (while there is a relation, the issues involved are a bit different). It then overviews proposed solutions to the problem of fake content detection, both algorithmic and human. On the algorithmic side, it focuses on work on classifiers. The chapter shows that most algorithmic approaches have significant issues, which has led to reliance on the human approach in spite of its known limitations (subjectivity, difficulty to scale). Finally, it closes with a discussion of potential future work.
Semi-Supervised Self-Training of Hate and Offensive Speech from Social Media
