Are Strong Baselines Enough? False News Detection with Machine Learning

Abstract

False news refers to false, fake, or misleading information presented as real news. In recent years, there has been a noticeable increase in false news on the Internet. The goal of this paper was to study the automatic detection of such false news using machine learning and natural language processing techniques and to determine which techniques work the most effectively. This article first studies what constitutes false news and how it differs from other types of misleading information. We also study the results achieved by other researchers on the same topic. After building a foundation to understand false news and the various ways of automatically detecting it, this article provides its own experiments. These experiments were carried out on four different datasets, one that was made just for this article, using 10 different machine learning methods. The results of this article were satisfactory and provided answers to the original research questions set up at the beginning of this article. This article could determine from the experiments that passive aggressive algorithms, support vector machines, and random forests are the most efficient methods for automatic false news detection. This article also concluded that more complex experiments, such as using multiple levels of identifying false news or detecting computer-generated false news, require more complex machine learning models.