用人工消除人工：Google Play上的暗模式和锁定行为
Erasing Labor with Labor: Dark Patterns and Lockstep Behaviors on Google Play
摘要：
Google Play的政策禁止使用奖励安装、评级和评论来操纵应用的位置。然而，仍然存在激励平台上其他应用安装的应用。为了了解安装激励应用程序如何影响用户，我们通过社会技术镜头检查其生态系统，并对其评论和权限进行混合方法分析。我们的数据集包含五个月内每天从60个此类应用程序中收集的31.9万条评论，累计安装量超过1.605亿次。我们对评论进行定性分析，以揭示开发人员在安装激励应用程序中采用的各种类型的黑暗模式，突出他们在用户和平台层面的规范性问题。这些应用程序请求的匿名验证了我们对暗模式的发现，超过92%的应用程序访问敏感用户信息。我们在安装激励应用程序上发现了欺诈性评论的证据，然后我们将其建模为应用程序和评论者的动态二分图中的边缘流。我们提出的国家的最先进的微集群异常检测算法的重新配置产生了可喜的初步结果，在检测这种欺诈行为。我们发现了旨在提高安装激励应用程序整体评级的评论所表现出的高度显著的锁步行为。在评估算法检测到的50个最可疑的提升评论集群时，我们发现（i）94%的评论几乎相同（47个集群），和（ii）超过35%（4，717篇评论中的1，687篇）在其集群中以相同的形式存在近乎相同的对。最后，我们讨论了欺诈如何与劳动力交织在一起，并对Google Play的信任和透明度构成威胁。
英文摘要：
Google Play's policy forbids the use of incentivized installs, ratings, and reviews to manipulate the placement of apps. However, there still exist apps that incentivize installs for other apps on the platform. To understand how install-incentivizing apps affect users, we examine their ecosystem through a socio-technical lens and perform a mixed-methods analysis of their reviews and permissions. Our dataset contains 319K reviews collected daily over five months from 60 such apps that cumulatively account for over 160.5M installs. We perform qualitative analysis of reviews to reveal various types of dark patterns that developers incorporate in install-incentivizing apps, highlighting their normative concerns at both user and platform levels. Permissions requested by these apps validate our discovery of dark patterns, with over 92% apps accessing sensitive user information. We find evidence of fraudulent reviews on install-incentivizing apps, following which we model them as an edge stream in a dynamic bipartite graph of apps and reviewers. Our proposed reconfiguration of a state-of-the-art microcluster anomaly detection algorithm yields promising preliminary results in detecting this fraud. We discover highly significant lockstep behaviors exhibited by reviews that aim to boost the overall rating of an install-incentivizing app. Upon evaluating the 50 most suspicious clusters of boosting reviews detected by the algorithm, we find (i) near-identical pairs of reviews across 94% (47 clusters), and (ii) over 35% (1,687 of 4,717 reviews) present in the same form near-identical pairs within their cluster. Finally, we conclude with a discussion on how fraud is intertwined with labor and poses a threat to the trust and transparency of Google Play.
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。
