Understanding Incentivized Mobile App Installs on Google Play Store
- URL: http://arxiv.org/abs/2010.01497v1
- Date: Sun, 4 Oct 2020 07:27:28 GMT
- Title: Understanding Incentivized Mobile App Installs on Google Play Store
- Authors: Shehroze Farooqi, \'Alvaro Feal, Tobias Lauinger, Damon McCoy, Zubair
Shafiq, Narseo Vallina-Rodriguez
- Abstract summary: "Incentivized" advertising platforms allow mobile app developers to acquire new users by directly paying users to install and engage with mobile apps.
Apple App Store and Google Play Store discourage incentivized installs because they can manipulate app store metrics.
We present the first study to understand the ecosystem of incentivized mobile app install campaigns in Android.
- Score: 16.095843448016552
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- What Is an App Store? The Software Engineering Perspective [8.551225450507687]
"App stores" are online software stores where end users may browse, purchase, download, and install software applications.
By far, the best known app stores are associated with mobile platforms, such as Google Play for Android and Apple's App Store for iOS.
Today, there is a rich diversity of app stores and these stores have largely been overlooked by researchers.
arXiv Detail & Related papers (2024-01-08T23:56:45Z) - To remove or not remove Mobile Apps? A data-driven predictive model
approach [4.853751680856816]
We propose a data-driven predictive approach that determines whether the respective app will be removed or accepted.
Our approach can support developers in improving their apps and users in downloading the ones that are less likely to be removed.
arXiv Detail & Related papers (2022-06-08T14:00:53Z) - Erasing Labor with Labor: Dark Patterns and Lockstep Behaviors on Google
Play [13.658284581863839]
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.
arXiv Detail & Related papers (2022-02-09T16:54:27Z) - Analysis of Longitudinal Changes in Privacy Behavior of Android
Applications [79.71330613821037]
In this paper, we examine the trends in how Android apps have changed over time with respect to privacy.
We examine the adoption of HTTPS, whether apps scan the device for other installed apps, the use of permissions for privacy-sensitive data, and the use of unique identifiers.
We find that privacy-related behavior has improved with time as apps continue to receive updates, and that the third-party libraries used by apps are responsible for more issues with privacy.
arXiv Detail & Related papers (2021-12-28T16:21:31Z) - RacketStore: Measurements of ASO Deception in Google Play via Mobile and
App Usage [20.13310058856793]
We present measurements from a study of 943 installs of RacketStore on 803 unique devices controlled by ASO providers and regular users.
We reveal significant differences between ASO providers and regular users in terms of the number and types of user accounts registered on their devices.
We show that they can train supervised learning algorithms to detect paid app installs and fake reviews with an F1-measure of 99.72%.
arXiv Detail & Related papers (2021-11-19T19:24:48Z) - Demystifying Removed Apps in iOS App Store [0.0]
This paper takes the initiative to conduct a large-scale and longitudinal study of removed apps in the iOS app store.
Our analysis reveals that although most of the removed apps are low-quality apps, a number of them are quite popular.
arXiv Detail & Related papers (2021-01-13T14:34:26Z) - Context-Aware Target Apps Selection and Recommendation for Enhancing
Personal Mobile Assistants [42.25496752260081]
This paper addresses two research problems that are vital for developing effective personal mobile assistants: target apps selection and recommendation.
Here we focus on context-aware models to leverage the rich contextual information available to mobile devices.
We propose a family of context-aware neural models that take into account the sequential, temporal, and personal behavior of users.
arXiv Detail & Related papers (2021-01-09T17:07:47Z) - Emerging App Issue Identification via Online Joint Sentiment-Topic
Tracing [66.57888248681303]
We propose a novel emerging issue detection approach named MERIT.
Based on the AOBST model, we infer the topics negatively reflected in user reviews for one app version.
Experiments on popular apps from Google Play and Apple's App Store demonstrate the effectiveness of MERIT.
arXiv Detail & Related papers (2020-08-23T06:34:05Z) - An Empirical Study of In-App Advertising Issues Based on Large Scale App
Review Analysis [67.58267006314415]
We present a large-scale analysis on ad-related user feedback from App Store and Google Play.
From a statistical analysis of 36,309 ad-related reviews, we find that users care most about the number of unique ads and ad display frequency during usage.
Some ad issue types are addressed more quickly by developers than other ad issues.
arXiv Detail & Related papers (2020-08-22T05:38:24Z) - Mind the GAP: Security & Privacy Risks of Contact Tracing Apps [75.7995398006171]
Google and Apple have jointly provided an API for exposure notification in order to implement decentralized contract tracing apps using Bluetooth Low Energy.
We demonstrate that in real-world scenarios the GAP design is vulnerable to (i) profiling and possibly de-anonymizing persons, and (ii) relay-based wormhole attacks that basically can generate fake contacts.
arXiv Detail & Related papers (2020-06-10T16:05:05Z) - General-Purpose User Embeddings based on Mobile App Usage [46.343844014289246]
behaviors on mobile app usage, including retention, installation, and uninstallation, can be a good indicator for both long-term and short-term interests of users.
Traditionally, user modeling from mobile app usage heavily relies on handcrafted feature engineering.
We present a tailored AutoEncoder-coupled Transformer Network (AETN), by which we overcome these challenges and achieve the goals of reducing manual efforts and boosting performance.
arXiv Detail & Related papers (2020-05-27T12:01:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.