Erasing Labor with Labor: Dark Patterns and Lockstep Behaviors on Google Play
Ashwin Singh, Arvindh Arun, Ayushi Jain, Pooja Desur, Pulak Malhotra,, Duen Horng Chau, Ponnurangam Kumaraguru

TL;DR
This study investigates dark patterns, fraudulent reviews, and lockstep behaviors in Google Play's incentivized install apps, revealing privacy concerns and proposing detection methods to enhance platform trust and transparency.
Contribution
It provides a socio-technical analysis of incentivized install apps, identifies dark patterns and fraudulent review behaviors, and introduces a novel detection approach for review fraud.
Findings
Over 92% of incentivized apps access sensitive user data.
Detected lockstep review behaviors with 94% near-identical pairs.
Proposed a reconfigured anomaly detection algorithm showing promising results.
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…
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