An Empirical Study of In-App Advertising Issues Based on Large Scale App Review Analysis
Cuiyun Gao, Jichuan Zeng, David Lo, Xin Xia, Irwin King, Michael R., Lyu

TL;DR
This study analyzes large-scale user feedback from app stores to identify and categorize ad-related issues, providing insights and strategies for developers to improve user experience while maintaining ad revenue.
Contribution
It presents a comprehensive classification of ad issues based on extensive user reviews and analyzes their impact on user ratings and fix durations across platforms.
Findings
Users are most concerned about ad frequency and number of unique ads.
Security and notification issues lead to lower user ratings.
Ad issue types and fix times vary significantly between platforms.
Abstract
In-app advertising closely relates to app revenue. Reckless ad integration could adversely impact app reliability and user experience, leading to loss of income. It is very challenging to balance the ad revenue and user experience for app developers. In this paper, 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…
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Taxonomy
TopicsSoftware Engineering Research · Web Data Mining and Analysis · Advanced Malware Detection Techniques
