Automatic Detection of Fake News

TL;DR

This paper introduces new datasets and machine learning experiments for automatically detecting fake news across multiple domains, addressing the challenge of misinformation in online media.

Contribution

It provides two novel datasets for fake news detection and explores linguistic differences, along with experiments on building and analyzing fake news classifiers.

Findings

New datasets covering seven news domains

Linguistic differences between fake and legitimate news identified

Effective fake news detection models developed

Abstract

The proliferation of misleading information in everyday access media outlets such as social media feeds, news blogs, and online newspapers have made it challenging to identify trustworthy news sources, thus increasing the need for computational tools able to provide insights into the reliability of online content. In this paper, we focus on the automatic identification of fake content in online news. Our contribution is twofold. First, we introduce two novel datasets for the task of fake news detection, covering seven different news domains. We describe the collection, annotation, and validation process in detail and present several exploratory analysis on the identification of linguistic differences in fake and legitimate news content. Second, we conduct a set of learning experiments to build accurate fake news detectors. In addition, we provide comparative analyses of the automatic and manual identification of fake news.