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.
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Han, J., Chen, K., Jiang, D. (2012). Probabilistically Ranking Web Article Quality Based on Evolution Patterns. In: Hameurlain, A., Küng, J., Wagner, R., Liddle, S.W., Schewe, KD., Zhou, X. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems VI. Lecture Notes in Computer Science, vol 7600. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34179-3_8
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DOI: https://doi.org/10.1007/978-3-642-34179-3_8
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