  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.
