Probabilistically ranking web article quality based on evolution patterns
|Probabilistically ranking web article quality based on evolution patterns|
|Author(s)||Han J., Chen K., Jiang D.|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||Unknown (Extra: ITS data, Learning evolution, State sequences, User-generated content, Web users, Wikipedia, Expert systems, Hidden Markov models)|
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Probabilistically ranking web article quality based on evolution patterns is a 2012 conference paper written in English by Han J., Chen K., Jiang D. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
User-generated content (UGC) is created, updated, and maintained by various web users, and its data quality is a major concern to all users. We observe that each Wikipedia page usually goes through a series of revision stages, gradually approaching a relatively steady quality state and that articles of different quality classes exhibit specific evolution patterns. We propose to assess the quality of a number of web articles using Learning Evolution Patterns (LEP). First, each article's revision history is mapped into a state sequence using the Hidden Markov Model (HMM). Second, evolution patterns are mined for each quality class, and each quality class is characterized by a set of quality corpora. Finally, an article's quality is determined probabilistically by comparing the article with the quality corpora. Our experimental results demonstrate that the LEP approach can capture a web article's quality precisely.
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