Investigating red packet fraud in Android applications: Insights from user reviews

TL;DR

This study investigates red packet fraud in Android apps by analyzing over 360,000 user reviews, revealing widespread fraudulent practices and their negative impact on user experience and app reputation.

Contribution

The paper introduces ReckDetector, an automated method to identify apps with red packets, and provides a comprehensive analysis of user-reported fraud issues using advanced NLP techniques.

Findings

Red packet fraud is highly prevalent in Android apps.

Fraudulent red packets significantly harm user experience.

Developers exploit red packets as deceptive incentives for profit.

Abstract

With the popularization of smartphones, red packets have been widely used in mobile apps. However, the issues of fraud associated with them have also become increasingly prominent. As reported in user reviews from mobile app markets, many users have complained about experiencing red packet fraud and being persistently troubled by fraudulent red packets. To uncover this phenomenon, we conduct the first investigation into an extensive collection of user reviews on apps with red packets. In this paper, we first propose a novel automated approach, ReckDetector, for effectively identifying apps with red packets from app markets. We then collect over 360,000 real user reviews from 334 apps with red packets available on Google Play and three popular alternative Android app markets. We preprocess the user reviews to extract those related to red packets and fine-tune a pre-trained BERT model to identify negative reviews. Finally, based on semantic analysis, we have summarized six distinct categories of red packet fraud issues reported by users. Through our study, we found that red packet fraud is highly prevalent, significantly impacting user experience and damaging the reputation of apps. Moreover, red packets have been widely exploited by unscrupulous app developers as a deceptive incentive mechanism to entice users into completing their designated tasks, thereby maximizing their profits.