A Survey on Automatic Fake News Identification Techniques for Online and Socially Produced Data

Fake news are spread on online sites and social media at an alarming speed and in large quantities. Fake news aim to mislead and deceive readers with verifiable false information and they are published on untrusted websites and social media accounts. As they have a very big impact on readers, it is critical to develop efficient models for detecting fake news. This paper reviews the literature on fake news detection and categorizes detection approaches into Knowledge Based approaches and Machine Learning based approaches. Machine Learning based approaches that have been covered in this paper are divided into Conventional approaches and Neural Network approaches. It provides Support Vector Machines (SVMs) and Naïve Bayes for Conventional approaches. In addition to Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for Neural Network approaches. Also, the paper discusses the provided approaches.