• DOI:10.1145/2872427.2883085
• Corpus ID: 30068

Disinformation on the Web: Impact, Characteristics, and Detection of Wikipedia Hoaxes

@article{Kumar2016DisinformationOT,
  title={Disinformation on the Web: Impact, Characteristics, and Detection of Wikipedia Hoaxes},
  author={Srijan Kumar and Robert West and Jure Leskovec},
  journal={Proceedings of the 25th International Conference on World Wide Web},
  year={2016}
}

• Srijan Kumar, Robert West, J. Leskovec
• Published 11 April 2016
• Computer Science
• Proceedings of the 25th International Conference on World Wide Web

Wikipedia is a major source of information for many people. However, false information on Wikipedia raises concerns about its credibility. One way in which false information may be presented on Wikipedia is in the form of hoax articles, i.e., articles containing fabricated facts about nonexistent entities or events. In this paper we study false information on Wikipedia by focusing on the hoax articles that have been created throughout its history. We make several contributions. First, we assess the real-world impact of hoax articles by measuring how long they survive before being debunked, how many pageviews they receive, and how heavily they are referred to by documents on the Web. We find that, while most hoaxes are detected quickly and have little impact on Wikipedia, a small number of hoaxes survive long and are well cited across the Web. Second, we characterize the nature of successful hoaxes by comparing them to legitimate articles and to failed hoaxes that were discovered shortly after being created. We find characteristic differences in terms of article structure and content, embeddedness into the rest of Wikipedia, and features of the editor who created the hoax. Third, we successfully apply our findings to address a series of classification tasks, most notably to determine whether a given article is a hoax. And finally, we describe and evaluate a task involving humans distinguishing hoaxes from non-hoaxes. We find that humans are not particularly good at the task and that our automated classifier outperforms them by a big margin.