Assessing quality values of Wikipedia articles using implicit positive and negative ratings
|Assessing quality values of Wikipedia articles using implicit positive and negative ratings|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||Edit History, Quality, Reputation, Wikipedia (Extra: Experimental evaluation, High quality, Low qualities, Quality value, Reputation, Survival ratio, Wikipedia, Image quality, Information management, Social networking (online))|
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Assessing quality values of Wikipedia articles using implicit positive and negative ratings is a 2012 conference paper written in English by Suzuki Y. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
In this paper, we propose a method to identify high-quality Wikipedia articles by mutually evaluating editors and text using implicit positive and negative ratings. One of major approaches for assessing Wikipedia articles is a text survival ratio based approach. However, the problem of this approach is that many low quality articles are misjudged as high quality, because of two issues. This is because, every editor does not always read the whole articles. Therefore, if there is a low quality text at the bottom of a long article, and the text have not seen by the other editors, then the text survives beyond many edits, and the survival ratio of the text is high. To solve this problem, we use a section or a paragraph as a unit of remaining instead of a whole page. This means that if an editor edits an article, the system treats that the editor gives positive ratings to the section or the paragraph that the editor edits. This is because, we believe that if editors edit articles, the editors may not read the whole page, but the editors should read the whole sections or paragraphs, and delete low-quality texts. From experimental evaluation, we confirmed that the proposed method could improve the accuracy of quality values for articles.
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