Wikipedia vandalism detection: Combining natural language, metadata, and reputation features
|Wikipedia vandalism detection: Combining natural language, metadata, and reputation features|
|Author(s)||Adler B.T., De Alfaro L., Mola-Velasco S.M., Rosso P., West A.G.|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||Unknown (Extra: Bad faith, Joint system, NAtural language processing, Natural languages, Online encyclopedia, Spatiotemporal analysis, Wikipedia, Computational linguistics, Natural language processing systems, Text processing, Word processing, Metadata)|
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Wikipedia vandalism detection: Combining natural language, metadata, and reputation features is a 2011 conference paper written in English by Adler B.T., De Alfaro L., Mola-Velasco S.M., Rosso P., West A.G. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
Wikipedia is an online encyclopedia which anyone can edit. While most edits are constructive, about 7% are acts of vandalism. Such behavior is characterized by modifications made in bad faith; introducing spam and other inappropriate content. In this work, we present the results of an effort to integrate three of the leading approaches to Wikipedia vandalism detection: a spatio-temporal analysis of metadata (STiki), a reputation-based system (WikiTrust), and natural language processing features. The performance of the resulting joint system improves the state-of-the-art from all previous methods and establishes a new baseline for Wikipedia vandalism detection. We examine in detail the contribution of the three approaches, both for the task of discovering fresh vandalism, and for the task of locating vandalism in the complete set of Wikipedia revisions.
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