Erasing Labor with Labor: Dark Patterns and Lockstep Behaviors on Google Play

• URL: http://arxiv.org/abs/2202.04561v2
• Date: Tue, 17 May 2022 22:10:54 GMT
• Title: Erasing Labor with Labor: Dark Patterns and Lockstep Behaviors on Google Play
• Authors: Ashwin Singh, Arvindh Arun, Ayushi Jain, Pooja Desur, Pulak Malhotra, Duen Horng Chau, Ponnurangam Kumaraguru
• Abstract summary: Google Play's policy forbids the use of incentivized installs, ratings, and reviews to manipulate the placement of apps. We examine install-incentivizing apps 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 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.
• Score: 13.658284581863839
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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