Cited by 22Spam and Phishing DetectionMobile and Web ApplicationsDigital Platforms and Economics

Understanding Incentivized Mobile App Installs on Google Play Store

Open access
Farooqi, Shehroze, Feal, Álvaro, Lauinger, Tobias, McCoy, Damon, Shafiq, Zubair, Vallina-Rodriguez, Narseo·2020·Proceedings of the ACM Internet Measurement Conference

Abstract

"Incentivized" advertising platforms allow mobile app developers to acquire new users by directly paying users to install and engage with mobile apps (e.g., create an account, make in-app purchases). Incentivized installs are banned by the Apple App Store and discouraged by the Google Play Store because they can manipulate app store metrics (e.g., install counts, appearance in top charts). Yet, many organizations still offer incentivized install services for Android apps. In this paper, we present the first study to understand the ecosystem of incentivized mobile app install campaigns in Android and its broader ramifications through a series of measurements. We identify incentivized install campaigns that require users to install an app and perform in-app tasks targeting manipulation of a wide variety of user engagement metrics (e.g., daily active users, user session lengths) and revenue. Our results suggest that these artificially inflated metrics can be effective in improving app store metrics as well as helping mobile app developers to attract funding from venture capitalists. Our study also indicates lax enforcement of the Google Play Store's existing policies to prevent these behaviors. It further motivates the need for stricter policing of incentivized install campaigns. Our proposed measurements can also be leveraged by the Google Play Store to identify potential policy violations.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1145/3419394.3423662

Cites

19 of 19 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • ASO workers lack interest in the apps that they promote.

    “Our work is partially motivated by Farooqi et al. [38]'s finding that ASO workers lack interest in the apps that they promote.”

    From RacketStore (2021) · cites this paper as [38]

    Section 3.2 (p. 5) says that most users who installed the study’s app were likely not genuinely interested in it and appeared to install it for the payout. That supports the claim’s central point, but the paper hedges its inference and concerns users in incentivized-install campaigns, rather than establishing that ASO workers generally lack interest in the apps they promote. (LLM, conf=0.85)

    Experimental — accuracy not yet measured
  • Farooqi et al. [38] have shown that incentivized app install platforms (IIP) are able to provide thousands of installs that successfully evade Google defenses.

    “In previous work, Farooqi et al. [38] have shown that incentivized app install platforms (IIP) are able to provide thousands of installs that successfully evade Google defenses.”

    From RacketStore (2021) · cites this paper as [38]

    Section 3.2 (pp. 4–5) reports 1,679 incentivized installs for the authors’ test app and an increase in its public Google Play install count from 0 to 1,000. Section 5.2 (p. 11) says these results suggest Google’s detection and filtering may fail; it does not establish that thousands of installs successfully evaded Google’s defenses. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 9

  1. [17][17] Michael Bailey, David Dittrich, Erin Kenneally, and Doug Maughan. 2012. The Menlo report. IEEE Security & Privacy 10 (2) (2012), 71-75. -
  2. [19][19] Juan Caballero, Chris Grier, Christian Kreibich, and Vern Paxson. 2011. Measuring pay-per-install: The commoditization of malware distribution. In USENIX Security Symposium . -
  3. [36][36] Platon Kotzias, Leyla Bilge, and Juan Caballero. 2016. Measuring PUP Prevalence and PUP Distribution through Pay-Per-Install Services. In USENIX Security Symposium . -
  4. [37][37] Ziang Ma, Haoyu Wang, Yao Guo, and Xiangqun Chen. 2016. LibRadar: fast and accurate detection of third-party libraries in Android apps. In International Conference on Software Engineering (ICSE) Companion . -
  5. [41][41] Alexander Moshchuk, Tanya Bragin, Steven D Gribble, and Henry M Levy. 2006. ACrawler-based Study of Spyware in the Web. In Network and Distributed Systems Security (NDSS) Symposium . -
  6. [48][48] Mizanur Rahman, Nestor Hernandez, Ruben Recabarren, Syed Ishtiaque Ahmed, and Bogdan Carbunar. 2019. The Art and Craft of Fraudulent App Promotion in Google Play. In ACM CCS . -
  7. [58][58] Gianluca Stringhini, Gang Wang, Manuel Egele, Christopher Kruegel, Giovanni Vigna, Haitao Zheng, and Ben Y. Zhao. 2013. Follow the Green: Growth and Dynamics in Twitter Follower Markets. In ACM Internet Measurement Conference (IMC) . -
  8. [61][61] Bimal Viswanath, M. Ahmad Bashir, Mark Crovella, Saikat Guha, Krishna P. Gummadi, Balachander Krishnamurthy, and Alan Mislove. 2014. Towards Detecting Anomalous User Behavior in Online Social Networks. In USENIX Security Symposium . -
  9. [65][65] Zhirong Liu Zhenhua Dong Jiebo Luo Yingtong Dou, Weijian Li and Philip S. Yu. 2019. Uncovering download fraud activities in mobile app markets. In IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining . -

Cited by

Sorted by citations

  • RacketStore

    Hernandez, N., Recabarren, R., Carbunar, B., & Ahmed, S. I. (2021) · Proceedings of the 21st ACM Internet Measurement Conference · doi:10.1145/3487552.3487837 · Cited by 3 · see citing works

    “Our work is partially motivated by Farooqi et al. [38]’s finding that ASO workers lack interest in the apps that they promote.”
    BackgroundMethodInfluential

Authors
Farooqi, S., Feal, Á., Lauinger, T., McCoy, D., Shafiq, Z., & Vallina-Rodriguez, N.
Affiliations
  • University of Iowa (US)
  • IMDEA Networks (ES)
  • New York University (US)
  • University of California, Davis (US)
  • IMDEA Networks Institute/ICSI
Venue
Proceedings of the ACM Internet Measurement Conference
Publisher
ACM
Published
2020-10-27
Pages
696-709
OpenAlex
W3091580460
Cited by
22
References
19
Citations by year
2026: 2 · 2025: 3 · 2024: 4 · 2023: 3 · 2022: 4 · 2021: 5 · 2020: 1
FWCI
2.46
Type
conference paper
Language
en
Open access
Locations
  • ACM Internet Measurement Conference (IMC) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • IMDEA Networks Institute Digital Repository (submittedVersion, other-oa)
  • Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
  • arXiv (Cornell University)
Funders
  • National Science Foundation (1564329, 1715152, 1814816, and 1954224)
  • European Union's Horizon 2020 Innovation Action Program (786741)
  • Spanish Ministry of Science and Innovation (PID2019-111429RB-C22)
  • Facebook
  • Google
Topics
  • Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
  • Physical Sciences › Computer Science › Information Systems › Mobile and Web Applications
  • Social Sciences › Business, Management and Accounting › Strategy and Management › Digital Platforms and Economics
Keywords
Google Play Store, Android application, in-app purchases, user engagement metrics, app store rankings, venture financing

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review