Publication Type

Journal Article

Version

submittedVersion

Publication Date

2-2022

Abstract

Context: In-app advertising closely relates to app revenue. Reckless ad integration could adversely impact app quality and user experience, leading to loss of income. It is very challenging to balance the ad revenue and user experience for app developers. Objective: Towards tackling the challenge, we conduct a study on analyzing user concerns about in-app advertisement. Method: Specifically, we present a large-scale analysis on ad-related user feedback. The large user feedback data from App Store and Google Play allow us to summarize ad-related app issues comprehensively and thus provide practical ad integration strategies for developers. We first define common ad issues by manually labeling a statistically representative sample of ad-related feedback, and then build an automatic classifier to categorize ad-related feedback. We study the relations between different ad issues and user ratings to identify the ad issues poorly scored by users. We also explore the fix durations of ad issues across platforms for extracting insights into prioritizing ad issues for ad maintenance. Results: (1) We summarize 15 types of ad issues by manually annotating 903 out of 36,309 ad-related user reviews. 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. (2) Users tend to give relatively lower ratings when they report the security and notification related issues. (3) Regarding different platforms, we observe that the distributions of ad issues are significantly different between App Store and Google Play. (4) Some ad issue types are addressed more quickly by developers than other ad issues. Conclusion: We believe the findings we discovered can benefit app developers towards balancing ad revenue and user experience while ensuring app quality.

Keywords

Ad issues, Cross platform, In-app ads, Mobile app, User reviews

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Information and Software Technology

Volume

142

First Page

1

Last Page

13

ISSN

0950-5849

Identifier

10.1016/j.infsof.2021.106741

Publisher

Elsevier

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1016/j.infsof.2021.106741

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