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.
| Original language | American English |
|---|---|
| Title of host publication | Disinformation, Misinformation, and Fake News in Social Media |
| Subtitle of host publication | Emerging Research Challenges and Opportunities |
| Editors | Kai Shu, Suhang Wang, Dongwon Lee, Huan Liu |
| Place of Publication | Cham |
| Publisher | Springer International Publishing AG |
| Pages | 163-182 |
| Number of pages | 20 |
| ISBN (Print) | 978-3-030-42699-6 |
| DOIs | |
| State | Published - 2020 |
| Externally published | Yes |