Automated Fake News detection using computational Forensic Linguistics

Abstract

In our society, everyone has access to the internet and can post anything about any topic at any time. Despite its many advantages, this possibility brought along a serious problem: Fake News. Fake News is news that is not real for not following journalism principles. Instead, Fake News try to mimic the look and feel of real news with the intent to disinform the reader. However, what makes Fake News a real problem is the influence that it can have on our society. Lay people are attracted to this kind of news and often give more attention to them than truthful accounts. Despite the development of systems to detect Fake News, most are based on fact-checking methods, which are unfit when the news's truth is distorted, exaggerated, or even placed out of context. We aim to detect Portuguese Fake News using machine learning techniques with a Forensic Linguistic approach. Contrary to previous approaches, our approach builds upon linguistic and stylistic analysis methods that have been tried and tested in Forensic Linguistic analysis. After collecting the corpus from multiple sources, we formulated the task as a text classification problem and demonstrated the proposed classifier's capability for detecting Fake News. The results reported are promising, achieving high accuracies on the test data.