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|Title:||Probabilistically ranking web article quality based on evolution patterns|
|Citation:||Han, J.,Chen, K.,Jiang, D. (2012). Probabilistically ranking web article quality based on evolution patterns. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7600 LNCS : 229-258. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-34179-3_8|
|Abstract:||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. © 2012 Springer-Verlag.|
|Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Appears in Collections:||Staff Publications|
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