Google Play Store'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 their users, we examine their ecosystem through a socio-technical lens and perform a longitudinal mixed-methods analysis of their reviews. We shortlist 60 install-incentivizing apps which collectively account for over 160.5M installs on the Google Play Store. We collect 1,000 most relevant reviews on these apps every day for a period of 52 days. First, our qualitative analysis reveals various types of dark patterns that developers incorporate in install-incentivizing apps to extort services and build market at the expense of their users. Second, we highlight the normative concerns of these dark patterns at both the individual and collective levels, elaborating on their detrimental effects on the price transparency and trust in the market of Google Play Store. Third, we uncover evidence of install-incentivizing apps indulging in review and rating fraud. Building upon our findings, we model apps and reviewers as networks and discover lockstep behaviors in the reviewing patterns that are strong indicators of review fraud. Fourth, we leverage the content information of reviews to find that reviewers who co-review more apps also show greater similarity in the content of their reviews, making them more suspicious. Finally, we conclude with a discussion on how our future work will generate implications for Google Play Store to prevent the exploitation of users while preserving transparency and trust in its market.
翻译:谷歌 Play Store 的政策禁止使用激励装置、评级和审查来操纵应用程序的安装。 但是, 仍然有各种应用程序来激励平台上安装其他应用程序。 为了了解安装激励应用程序如何影响用户, 我们通过社会技术透镜来检查其生态系统, 并对其审查进行纵向混合方法分析。 我们将60个安装激励应用程序放在谷歌 Play Store 上安装了160.5M 以上的软件。 我们在52天的时间里每天收集1,000份与这些应用程序最相关的审查。 首先, 我们的质量分析揭示了各种黑暗模式, 开发者在安装激励应用应用程序来敲诈服务并建立市场时, 牺牲了用户的利益。 其次, 我们强调个人和集体层面这些黑暗模式的规范问题, 阐述它们对谷歌 Play Store 市场价格透明度和信任的有害影响。 第三, 我们发现有证据表明,在52天期间,我们每天对这些应用程序进行1,000份最相关的审查。 首先,我们的质量分析揭示了各种黑暗模式, 在审查过程中,我们用更强的网络来创建和评级内容。 最终,我们通过对数据库进行审视,我们将会发现更多的数据审查, 来评估。