Abstract
App stores include an increasing amount of user feedback in form of app ratings and reviews. Research and recently also tool vendors have proposed analytics and data mining solutions to leverage this feedback to developers and analysts, e.g., for supporting release decisions. Research also showed that positive feedback improves apps’ downloads and sales figures and thus their success. As a side effect, a market for fake, incentivized app reviews emerged with yet unclear consequences for developers, app users, and app store operators. This paper studies fake reviews, their providers, characteristics, and how well they can be automatically detected. We conducted disguised questionnaires with 43 fake review providers and studied their review policies to understand their strategies and offers. By comparing 60,000 fake reviews with 62 million reviews from the Apple App Store we found significant differences, e.g., between the corresponding apps, reviewers, rating distribution, and frequency. This inspired the development of a simple classifier to automatically detect fake reviews in app stores. On a labelled and imbalanced dataset including one-tenth of fake reviews, as reported in other domains, our classifier achieved a recall of 91% and an AUC/ROC value of 98%. We discuss our findings and their impact on software engineering, app users, and app store operators.
Highlights
In app stores, users can rate downloaded apps on a scale from 1 to 5 stars and write a review message
We consider the collected reviews as fake for the following reasons: First, app store operators strongly require that app reviews must be 1) written by real users of the app and 2) cannot be incentivized
Even if reviewers are allowed to submit their honest opinion according to the review policy of this exchange portal, rewarded, incentivized, or non-spontaneous reviews are prohibited by the official Google and Apple App Store Review Guidelines (Apple 2017; Google 2018)
Summary
Users can rate downloaded apps on a scale from 1 to 5 stars and write a review message. Thereby, they can express satisfaction or dissatisfaction, report bugs, or suggest new features (Carreno and Winbladh 2013; Pagano and Maalej 2013; Maalej et al 2016a). Get paid or rewarded to submit reviews. They might or might not be real users of the app. Their review might or might not be correct and reflecting their opinion
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