In an online review system, a user writes a review with the intention of helping fellow consumers (i.e. the readers) make informed decision. However, product owners often provide incentives (e.g. coupons, bonus points, referral rewards) to the writers, motivating the writing of biased reviews. These biased reviews, while beneficial for both the writers and the owners, pollute the review space and destroy the readers’ trust significantly. In this paper, we analyze incentivized reviews in the Google Play store and identify a wide range of anomalous review types, including abusive reviews to damage the reputation of other apps in the review system. We find that there are groups of users that have been consistently taking part in such abusive actions. We further find that such incentivized reviews indeed help the apps in gaining popularity when compared to apps that do not receive incentivized reviews. We also identify an increasing trend in the number of apps being targeted by abusers, which, if continued, will render review systems as crowd advertising platforms rather than the unbiased source of helpful information.
This dataset is password protected; please email nabuelrub AT unm DOT edu to request access.
The code is available on my Github.
Anomalous Reviews Owing to Referral Incentive PDF
Noor Abu-El-Rub, Amanda Minnich and Abdullah Mueen, to appear In the Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017.
Impact of Referral Incentives on Mobile App Reviews PDF
Noor Abu-El-Rub, Amanda Minnich and Abdullah Mueen, In the Proceedings of the 17th International Conference on Web Engineering, ICWE 2017.