Understanding in-app advertising issues based on large scale app review analysis

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, w...

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Main Authors: GAO, Cuiyun, ZENG, Jichuan, LO, David, XIA, Xin, KING, Irwin, LYU, Michael R.
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2022
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Online Access:https://ink.library.smu.edu.sg/sis_research/6414
https://ink.library.smu.edu.sg/context/sis_research/article/7417/viewcontent/in_app_adv_sv.pdf
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-74172021-11-25T08:47:04Z Understanding in-app advertising issues based on large scale app review analysis GAO, Cuiyun ZENG, Jichuan LO, David XIA, Xin KING, Irwin LYU, Michael R. 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. 2022-02-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6414 info:doi/10.1016/j.infsof.2021.106741 https://ink.library.smu.edu.sg/context/sis_research/article/7417/viewcontent/in_app_adv_sv.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Ad issues Cross platform In-app ads Mobile app User reviews Databases and Information Systems Numerical Analysis and Scientific Computing Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Ad issues
Cross platform
In-app ads
Mobile app
User reviews
Databases and Information Systems
Numerical Analysis and Scientific Computing
Software Engineering
spellingShingle Ad issues
Cross platform
In-app ads
Mobile app
User reviews
Databases and Information Systems
Numerical Analysis and Scientific Computing
Software Engineering
GAO, Cuiyun
ZENG, Jichuan
LO, David
XIA, Xin
KING, Irwin
LYU, Michael R.
Understanding in-app advertising issues based on large scale app review analysis
description 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.
format text
author GAO, Cuiyun
ZENG, Jichuan
LO, David
XIA, Xin
KING, Irwin
LYU, Michael R.
author_facet GAO, Cuiyun
ZENG, Jichuan
LO, David
XIA, Xin
KING, Irwin
LYU, Michael R.
author_sort GAO, Cuiyun
title Understanding in-app advertising issues based on large scale app review analysis
title_short Understanding in-app advertising issues based on large scale app review analysis
title_full Understanding in-app advertising issues based on large scale app review analysis
title_fullStr Understanding in-app advertising issues based on large scale app review analysis
title_full_unstemmed Understanding in-app advertising issues based on large scale app review analysis
title_sort understanding in-app advertising issues based on large scale app review analysis
publisher Institutional Knowledge at Singapore Management University
publishDate 2022
url https://ink.library.smu.edu.sg/sis_research/6414
https://ink.library.smu.edu.sg/context/sis_research/article/7417/viewcontent/in_app_adv_sv.pdf
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