维基百科读者导航：当合成数据就足够了
Wikipedia Reader Navigation: When Synthetic Data Is Enough
摘要：
每天都有数百万人阅读维基百科。当使用超链接导航可用主题的广阔空间时，读者在文章网络上描述轨迹。了解这些导航模式对于更好地满足读者的需求并解决结构性偏见和知识差距至关重要。然而，由于维基百科承诺不存储或共享潜在的敏感数据来保护读者的隐私，因此缺乏公开可用的数据，阻碍了对维基百科导航的系统研究。在本文中，我们要问：维基百科读者的导航如何通过使用公开可用的资源来近似，最明显的是维基百科的点击流数据？我们系统地量化了真实导航序列和从点击流数据生成的合成序列之间的差异，在8个维基百科语言版本的6个分析中。总的来说，我们发现真实序列和合成序列之间的差异在统计学上是显著的，但效应量很小，通常远低于10%。这构成了维基百科点击流数据作为公共资源的效用的定量证据：点击流数据可以密切捕获维基百科上的读者导航，并为依赖于读者数据的大多数实际下游应用提供了足够的近似。更广泛地说，这项研究提供了一个例子，说明点击流类数据如何在保护用户隐私的同时，普遍支持对在线平台上用户导航的研究。
英文摘要：
Every day millions of people read Wikipedia. When navigating the vast space of available topics using hyperlinks, readers describe trajectories on the article network. Understanding these navigation patterns is crucial to better serve readers' needs and address structural biases and knowledge gaps. However, systematic studies of navigation on Wikipedia are hindered by a lack of publicly available data due to the commitment to protect readers' privacy by not storing or sharing potentially sensitive data. In this paper, we ask: How well can Wikipedia readers' navigation be approximated by using publicly available resources, most notably the Wikipedia clickstream data? We systematically quantify the differences between real navigation sequences and synthetic sequences generated from the clickstream data, in 6 analyses across 8 Wikipedia language versions. Overall, we find that the differences between real and synthetic sequences are statistically significant, but with small effect sizes, often well below 10%. This constitutes quantitative evidence for the utility of the Wikipedia clickstream data as a public resource: clickstream data can closely capture reader navigation on Wikipedia and provides a sufficient approximation for most practical downstream applications relying on reader data. More broadly, this study provides an example for how clickstream-like data can generally enable research on user navigation on online platforms while protecting users' privacy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
