Liu Wang, Haoyu Wang, Xiapu Luo, Tao Zhang, Shangguang Wang, Xuanzhe Liu
{"title":"破解iOS应用商店中的“删除评论","authors":"Liu Wang, Haoyu Wang, Xiapu Luo, Tao Zhang, Shangguang Wang, Xuanzhe Liu","doi":"10.1145/3540250.3558966","DOIUrl":null,"url":null,"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.","PeriodicalId":68155,"journal":{"name":"软件产业与工程","volume":"219 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Demystifying “removed reviews” in iOS app store\",\"authors\":\"Liu Wang, Haoyu Wang, Xiapu Luo, Tao Zhang, Shangguang Wang, Xuanzhe Liu\",\"doi\":\"10.1145/3540250.3558966\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":68155,\"journal\":{\"name\":\"软件产业与工程\",\"volume\":\"219 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"软件产业与工程\",\"FirstCategoryId\":\"1089\",\"ListUrlMain\":\"https://doi.org/10.1145/3540250.3558966\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"软件产业与工程","FirstCategoryId":"1089","ListUrlMain":"https://doi.org/10.1145/3540250.3558966","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.
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