QualityRank: assessing quality of wikipedia articles by mutually evaluating editors and texts

Yumiko Suzuki, Masatoshi Yoshikawa
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引用次数: 8

Abstract

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.
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QualityRank:通过相互评估编辑和文本来评估维基百科文章的质量
在本文中,我们提出了一种通过相互评估编辑和文本来识别高质量维基百科文章的方法。使用编辑历史评估文章的主要方法是基于文本存活率的方法。但问题是,很多高质量的文章被认定为低质量,因为很多破坏者删除了高质量的文本,那么高质量文本的存活率就被破坏者降低了。我们的方法最大的优点是它能抵抗破坏行为。使用我们的方法,如果我们使用编辑器质量值来计算文本质量值,破坏者不会影响其他编辑器的任何质量值,那么文本质量值的准确性应该会提高。然而,问题是编辑器质量值是由文本质量值计算的,而文本质量值是由编辑器质量值计算的。为了解决这个问题,我们相互计算编辑器和文本质量值,直到它们收敛。使用这种方法,我们可以计算文本的质量值,其中考虑了编辑的质量值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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HT '22: 33rd ACM Conference on Hypertext and Social Media, Barcelona, Spain, 28 June 2022- 1 July 2022 HT '21: 32nd ACM Conference on Hypertext and Social Media, Virtual Event, Ireland, 30 August 2021 - 2 September 2021 HT '20: 31st ACM Conference on Hypertext and Social Media, Virtual Event, USA, July 13-15, 2020 Detecting Changes in Suicide Content Manifested in Social Media Following Celebrity Suicides. QualityRank: assessing quality of wikipedia articles by mutually evaluating editors and texts
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