QualityRank: Assessing quality of wikipedia articles by mutually evaluating editors and texts
|QualityRank: Assessing quality of wikipedia articles by mutually evaluating editors and texts|
|Author(s)||Suzuki Y., Yoshikawa M.|
|Published in||HT'12 - Proceedings of 23rd ACM Conference on Hypertext and Social Media|
|Keyword(s)||Edit history, Link analysis, Peer review, Quality (Extra: Experimental evaluation, High quality, Link analysis, Low qualities, Peer review, Quality value, Survival ratio, Text qualities, Wikipedia, Hypertext systems, Image quality, Problem solving, Quality control, Social networking (online))|
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QualityRank: Assessing quality of wikipedia articles by mutually evaluating editors and texts is a 2012 conference paper written in English by Suzuki Y., Yoshikawa M. and published in HT'12 - Proceedings of 23rd ACM Conference on Hypertext and Social Media.
In this paper, we propose a method to identify high-quality Wikipedia articles by mutually evaluating editors and texts. A major approach for assessing articles using edit history is a text survival ratio based approach. However, the problem is that many high-quality articles are identified as low quality, because many vandals delete high-quality texts, then the survival ratios of high-quality texts are decreased by vandals. Our approach's strongest point is its resistance to vandalism. Using our method, if we calculate text quality values using editor quality values, vandals do not affect any quality values of the other editors, then the accuracy of text quality values should improve. However, the problem is that editor quality values are calculated by text quality values, and text quality values are calculated by editor quality values. To solve this problem, we mutually calculate editor and text quality values until they converge. Using this method, we can calculate a quality value of a text that takes into consideration that of its editors. From experimental evaluation, we confirmed that the proposed method can improve the accuracy of quality values for articles. Copyright 2012 ACM.
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