How do metrics of link analysis correlate to quality, relevance and popularity in Wikipedia?
|How do metrics of link analysis correlate to quality, relevance and popularity in Wikipedia?|
|Author(s)||Hanada R.T.S., Cristo M., Pimentel M.D.G.C.|
|Published in||WebMedia 2013 - Proceedings of the 19th Brazilian Symposium on Multimedia and the Web|
|Keyword(s)||information retrieval, link analysis, quality of content, wikipedia (Extra: Content qualities, Human expert, Link analysis, Link analysis algorithms, Quality of contents, Wikipedia, Wikipedia articles, Factor analysis, Information retrieval, Search engines, Websites, Quality control)|
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How do metrics of link analysis correlate to quality, relevance and popularity in Wikipedia? is a 2013 conference paper written in English by Hanada R.T.S., Cristo M., Pimentel M.D.G.C. and published in WebMedia 2013 - Proceedings of the 19th Brazilian Symposium on Multimedia and the Web.
Many links between Web pages can be viewed as indicative of the quality and importance of the pages they pointed to. Accordingly, several studies have proposed metrics based on links to infer web page content quality. However, as far as we know, the only work that has examined the correlation between such metrics and content quality consisted of a limited study that left many open questions. In spite of these metrics having been shown successful in the task of ranking pages which were provided as answers to queries submitted to search engines, it is not possible to determine the specific contribution of factors such as quality, popularity, and importance to the results. This difficulty is partially due to the fact that such information is hard to obtain for Web pages in general. Unlike ordinary Web pages, the quality, importance and popularity of Wikipedia articles are evaluated by human experts or might be easily estimated. Thus, it is feasible to verify the relation between link analysis metrics and such factors in Wikipedia articles, our goal in this work. To accomplish that, we implemented several link analysis algorithms and compared their resulting rankings with the ones created by human evaluators regarding factors such as quality, popularity and importance. We found that the metrics are more correlated to quality and popularity than to importance, and the correlation is moderate.
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