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.

翻译：随着智能手机的普及，红包在移动应用中被广泛使用。然而，与之相关的欺诈问题也日益凸显。根据移动应用市场的用户评论报告，许多用户抱怨遭遇红包欺诈，并持续受到欺诈性红包的困扰。为揭示这一现象，我们对包含红包功能的应用进行了首次大规模用户评论调查。本文首先提出了一种新颖的自动化方法——ReckDetector，用于有效识别应用市场中包含红包功能的应用。随后，我们从Google Play及三个主流替代Android应用市场中，收集了334款含红包功能应用的超过36万条真实用户评论。我们对用户评论进行预处理，提取与红包相关的评论，并微调预训练的BERT模型以识别负面评价。最后，基于语义分析，我们总结了用户报告的六类不同的红包欺诈问题。通过研究，我们发现红包欺诈现象极为普遍，严重影响了用户体验并损害了应用声誉。此外，红包已被不良应用开发者广泛利用，作为一种欺骗性激励手段，诱使用户完成其指定的任务，从而最大化其利润。