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Demystifying “removed reviews” in iOS app store

Published: 09 November 2022 Publication History
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  • Abstract

    The app markets enable users to submit feedback for downloaded apps in the form of star ratings and text reviews, which are meant to be helpful and trustworthy for decision making to both developers and other users. App markets have released strict guidelines/policies for user review submissions. However, there has been growing evidence showing the untrustworthy and poor-quality of app reviews, making the app store review environment a shambles. Therefore, review removal is a common practice, and market maintainers have to remove undesired reviews from the market periodically in a reactive manner. Although some reports and news outlets have mentioned removed reviews, our research community still lacks the comprehensive understanding of the landscape of this kind of reviews. To fill the void, in this paper, we present a large-scale and longitudinal study of removed reviews in iOS App Store. We first collaborate with our industry partner to collect over 30 million removed reviews for 33,665 popular apps over the course of a full year in 2020. This comprehensive dataset enables us to characterize the overall landscape of removed reviews. We next investigate the practical reasons leading to the removal of policy-violating reviews, and summarize several interesting reasons, including fake reviews, offensive reviews, etc. More importantly, most of these mis-behaviors can be reflected on reviews’ basic information including the posters, narrative content, and posting time. It motivates us to design an automated approach to flag the policy-violation reviews, and our experiment result on the labelled benchmark can achieve a good performance (F1=97%). We further make an attempt to apply our approach to the large-scale industry setting, and the result suggests the promising industry usage scenario of our approach. Our approach can act as a gatekeeper to pinpoint policy-violation reviews beforehand, which will be quite effective in improving the maintenance process of app reviews in the industrial setting.

    References

    [1]
    2016. How to edit and delete App Store reviews on iPhone and iPad. https://theappfactor.com/how-to-edit-and-delete-app-store-reviews-on-iphone-and-ipad/
    [2]
    2017. How to Work With App Store Reviews. https://splitmetrics.com/blog/app-store-reviews/
    [3]
    2019. Fake App Reviews Cause App Store Concerns. https://blog.gummicube.com/2019/07/fake-app-reviews-cause-app-store-concerns
    [4]
    2020. It’s 2020 and the Google Play Store still has a major fake review problem. https://www.androidauthority.com/play-store-fake-review-problem-1082191/
    [5]
    2021. How to remove spam and fake iOS and Android reviews. https://appfollow.io/blog/how-to-remove-fake-and-inappropriate-app-reviews-in-the-app-store-and-google-play.
    [6]
    2021. Is Apple App Store Is Ignoring Purchased Fake Reviews? How Dating Apps are Abusing This. https://goodmenproject.com/technology/is-apple-app-store-is-ignoring-purchased-fake-reviews-how-dating-apps-are-abusing-this/.
    [7]
    2021. Levenshtein distance. https://en.wikipedia.org/wiki/Levenshtein_distance
    [8]
    2021. scikit-learn. https://scikit-learn.org/stable/
    [9]
    Naila Aslam, Waheed Yousuf Ramay, Kewen Xia, and Nadeem Sarwar. 2020. Convolutional neural network based classification of app reviews. IEEE Access, 8 (2020), 185619–185628.
    [10]
    Laura V Galvis Carreno and Kristina Winbladh. 2013. Analysis of user comments: an approach for software requirements evolution. In 2013 35th international conference on software engineering (ICSE). 582–591.
    [11]
    Ning Chen, Jialiu Lin, Steven CH Hoi, Xiaokui Xiao, and Boshen Zhang. 2014. AR-miner: mining informative reviews for developers from mobile app marketplace. In Proceedings of the 36th international conference on software engineering. 767–778.
    [12]
    Michael Crawford, Taghi M Khoshgoftaar, Joseph D Prusa, Aaron N Richter, and Hamzah Al Najada. 2015. Survey of review spam detection using machine learning techniques. Journal of Big Data, 2, 1 (2015), 1–24.
    [13]
    Xian Fan, Xiaoge Li, Feihong Du, Xin Li, and Mian Wei. 2016. Apply word vectors for sentiment analysis of APP reviews. In 2016 3rd International Conference on Systems and Informatics (ICSAI). 1062–1066.
    [14]
    Cuiyun Gao, Jichuan Zeng, David Lo, Chin-Yew Lin, Michael R Lyu, and Irwin King. 2018. Infar: Insight extraction from app reviews. In Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 904–907.
    [15]
    Emitza Guzman and Walid Maalej. 2014. How do users like this feature? a fine grained sentiment analysis of app reviews. In 2014 IEEE 22nd international requirements engineering conference (RE). 153–162.
    [16]
    Yangyu Hu, Haoyu Wang, Ren He, Li Li, Gareth Tyson, Ignacio Castro, Yao Guo, Lei Wu, and Guoai Xu. 2020. Mobile app squatting. In Proceedings of The Web Conference 2020. 1727–1738.
    [17]
    Yangyu Hu, Haoyu Wang, Li Li, Yao Guo, Guoai Xu, and Ren He. 2019. Want to earn a few extra bucks? a first look at money-making apps. In 2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER). 332–343.
    [18]
    Yangyu Hu, Haoyu Wang, Yajin Zhou, Yao Guo, Li Li, Bingxuan Luo, and Fangren Xu. 2019. Dating with scambots: Understanding the ecosystem of fraudulent dating applications. IEEE Transactions on Dependable and Secure Computing.
    [19]
    Apple Inc. 2021. App Store Review Guidelines. https://developer.apple.com/app-store/review/guidelines/
    [20]
    Apple Inc. 2021. Apple Media Services Terms and Conditions. https://www.apple.com/legal/internet-services/itunes/
    [21]
    Raymond YK Lau, SY Liao, Ron Chi-Wai Kwok, Kaiquan Xu, Yunqing Xia, and Yuefeng Li. 2012. Text mining and probabilistic language modeling for online review spam detection. ACM Transactions on Management Information Systems (TMIS), 2, 4 (2012), 1–30.
    [22]
    Li Li, Tegawendé F Bissyandé, and Jacques Klein. 2019. Rebooting research on detecting repackaged android apps: Literature review and benchmark. IEEE Transactions on Software Engineering.
    [23]
    Fuqi Lin, Haoyu Wang, Liu Wang, and Xuanzhe Liu. 2021. A Longitudinal Study of Removed Apps in iOS App Store. In Proceedings of the Web Conference 2021. 1435–1446.
    [24]
    Yuming Lin, Tao Zhu, Xiaoling Wang, Jingwei Zhang, and Aoying Zhou. 2014. Towards online review spam detection. In Proceedings of the 23rd International Conference on World Wide Web. 341–342.
    [25]
    Google LLC. 2021. Policy Center - User Ratings, Reviews, and Installs. https://support.google.com/googleplay/android-developer/answer/9898684
    [26]
    Walid Maalej, Zijad Kurtanović, Hadeer Nabil, and Christoph Stanik. 2016. On the automatic classification of app reviews. Requirements Engineering, 21, 3 (2016), 311–331.
    [27]
    Walid Maalej and Hadeer Nabil. 2015. Bug report, feature request, or simply praise? on automatically classifying app reviews. In 2015 IEEE 23rd international requirements engineering conference (RE). 116–125.
    [28]
    Yichuan Man, Cuiyun Gao, Michael R Lyu, and Jiuchun Jiang. 2016. Experience report: Understanding cross-platform app issues from user reviews. In 2016 IEEE 27th International Symposium on Software Reliability Engineering (ISSRE). 138–149.
    [29]
    Daniel Martens and Walid Maalej. 2019. Towards understanding and detecting fake reviews in app stores. Empirical Software Engineering, 24, 6 (2019), 3316–3355.
    [30]
    Dennis Pagano and Walid Maalej. 2013. User feedback in the appstore: An empirical study. In 2013 21st IEEE international requirements engineering conference (RE). 125–134.
    [31]
    Sebastiano Panichella, Andrea Di Sorbo, Emitza Guzman, Corrado A Visaggio, Gerardo Canfora, and Harald C Gall. 2015. How can i improve my app? classifying user reviews for software maintenance and evolution. In 2015 IEEE international conference on software maintenance and evolution (ICSME). 281–290.
    [32]
    Sakshi Ranjan and Subhankar Mishra. 2020. Comparative sentiment analysis of App reviews. In 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). 1–7.
    [33]
    Abbas Razaghpanah, Rishab Nithyanand, Narseo Vallina-Rodriguez, Srikanth Sundaresan, Mark Allman, Christian Kreibich, and Phillipa Gill. 2018. Apps, trackers, privacy, and regulators: A global study of the mobile tracking ecosystem.
    [34]
    Haoyu Wang, Hao Li, and Yao Guo. 2019. Understanding the evolution of mobile app ecosystems: A longitudinal measurement study of google play. In The World Wide Web Conference. 1988–1999.
    [35]
    Haoyu Wang, Zhe Liu, Yao Guo, Xiangqun Chen, Miao Zhang, Guoai Xu, and Jason Hong. 2017. An explorative study of the mobile app ecosystem from app developers’ perspective. In Proceedings of the 26th International Conference on World Wide Web. 163–172.
    [36]
    Haoyu Wang, Junjun Si, Hao Li, and Yao Guo. 2019. RmvDroid: Towards a Reliable Android Malware Dataset with App Metadata. In Proceedings of the 16th International Conference on Mining Software Repositories. 404–408.
    [37]
    Sihong Xie, Guan Wang, Shuyang Lin, and Philip S Yu. 2012. Review spam detection via time series pattern discovery. In Proceedings of the 21st International Conference on World Wide Web. 635–636.
    [38]
    Monali Zende and Aruna Gupta. 2016. Ranking Fraud and Fake Reviews Detection for Mobile Apps. International Journal of Advanced Research in Computer Science, 7, 3 (2016).
    [39]
    Xian Zhan, Lingling Fan, Tianming Liu, Sen Chen, Li Li, Haoyu Wang, Yifei Xu, Xiapu Luo, and Yang Liu. 2020. Automated Third-Party Library Detection for Android Applications: Are We There Yet? In 2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE). 919–930.
    [40]
    Yao Zhu, Hongzhi Liu, Yingpeng Du, and Zhonghai Wu. 2021. IFSpard: An Information Fusion-based Framework for Spam Review Detection. In Proceedings of the Web Conference 2021. 507–517.

    Cited By

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    • (2023)A Study of Gender Discussions in Mobile Apps2023 IEEE/ACM 20th International Conference on Mining Software Repositories (MSR)10.1109/MSR59073.2023.00086(598-610)Online publication date: May-2023

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    cover image ACM Conferences
    ESEC/FSE 2022: Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering
    November 2022
    1822 pages
    ISBN:9781450394130
    DOI:10.1145/3540250
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Publication History

    Published: 09 November 2022

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    Author Tags

    1. app store
    2. iOS
    3. removed review
    4. user review

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    • (2023)A Study of Gender Discussions in Mobile Apps2023 IEEE/ACM 20th International Conference on Mining Software Repositories (MSR)10.1109/MSR59073.2023.00086(598-610)Online publication date: May-2023

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