Fake News Detection by Weakly Supervised Learning Based on Content Features

Abstract

Fake news, defined as the publication of false information, either unintentional or with the intent to deceive or harm, is one of the important issues that affects today’s digital society significantly. All around the world, journalists and fact checking organizations are trying to fight this problem manually. However, fighting fake news is a time-sensitive task. Once leaked, fake news spreads fast and its impact on society increases. Because of the complex and dynamic nature of news, applying artificial intelligence methods to address the automatic detection of fake news is a challenging task. This work explores the use of weak supervised learning for fake news detection by using only the content of news articles. This is particularly important when the contextual information is not available or difficult to obtain quickly. To our knowledge, this is the first work which uses a content-based approach in weak supervised learning without the use of any contextual information for fake news detection. We propose an architecture that generates weak labels. We explore the effect of using weak labels for fake news detection with five different machine learning models. We demonstrate that weakly supervised learning is an effective approach to the automated detection of fake news in the absence of high quality labels.