Credibility-based Fake News Detection

TL;DR

This paper proposes a credibility-based approach for fake news detection, emphasizing the importance of source and author credibility indicators, such as author history and number of authors, to improve detection accuracy.

Contribution

It introduces a novel fake news detection method that leverages source and author credibility metrics, complementing traditional content-based approaches.

Findings

Author history correlates with fake news likelihood

Number of authors influences credibility assessment

Source credibility significantly improves detection accuracy

Abstract

Fake news can significantly misinform people who often rely on online sources and social media for their information. Current research on fake news detection has mostly focused on analyzing fake news content and how it propagates on a network of users. In this paper, we emphasize the detection of fake news by assessing its credibility. By analyzing public fake news data, we show that information on news sources (and authors) can be a strong indicator of credibility. Our findings suggest that an author's history of association with fake news, and the number of authors of a news article, can play a significant role in detecting fake news. Our approach can help improve traditional fake news detection methods, wherein content features are often used to detect fake news.