Center for Mathematical Modeling and Data Science, Osaka University, Toyonaka, Osaka, 560-8531, Japan.
Department of Network and Data Science, Central European University, Quellenstrasse 51, 1100, Vienna, Austria.
Sci Rep. 2021 Sep 15;11(1):18371. doi: 10.1038/s41598-021-97755-w.
Wikipedia, a paradigmatic example of online knowledge space is organized in a collaborative, bottom-up way with voluntary contributions, yet it maintains a level of reliability comparable to that of traditional encyclopedias. The lack of selected professional writers and editors makes the judgement about quality and trustworthiness of the articles a real challenge. Here we show that a self-consistent metrics for the network defined by the edit records captures well the character of editors' activity and the articles' level of complexity. Using our metrics, one can better identify the human-labeled high-quality articles, e.g., "featured" ones, and differentiate them from the popular and controversial articles. Furthermore, the dynamics of the editor-article system is also well captured by the metrics, revealing the evolutionary pathways of articles and diverse roles of editors. We demonstrate that the collective effort of the editors indeed drives to the direction of article improvement.
维基百科是在线知识空间的典范,它以协作、自下而上的方式组织,依靠自愿贡献,却保持着与传统百科全书相当的可靠性。由于缺乏精选的专业作家和编辑,因此对文章的质量和可信度进行判断是一项真正的挑战。在这里,我们表明,通过编辑记录定义的网络的自洽度量能够很好地捕捉编辑活动的特征和文章的复杂程度。使用我们的指标,可以更好地识别出经过人工标记的高质量文章,例如“特色”文章,并将其与受欢迎和有争议的文章区分开来。此外,指标还很好地捕捉到了编辑-文章系统的动态,揭示了文章的演进路径和编辑的多样化角色。我们证明,编辑的集体努力确实朝着改善文章的方向发展。