Investigating red packet fraud in android applications: insights from user reviews
Open Access
- 14 January 2026
- journal article
- research article
- Published by Springer Nature in Cybersecurity
- Vol. 9 (1) , 104
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.Keywords
All Related Versions
Funding Information
- National Natural Science Foundation of China (61972082)
- National Natural Science Foundation of China (62172202)
This publication has 26 references indexed in Scilit:
- Mining user privacy concern topics from app reviewsJournal of Systems and Software, 2025
- ReckDroid: Detecting red packet fraud in Android appsComputers & Security, 2024
- RoseMatcher: Identifying the impact of user reviews on app updatesInformation and Software Technology, 2023
- A large scale analysis of mHealth app user reviewsEmpirical Software Engineering, 2022
- Automatic Classification of Accessibility User Reviews in Android Apps2022
- DaPanda: Detecting Aggressive Push Notifications in Android Apps2019
- Towards understanding and detecting fake reviews in app storesEmpirical Software Engineering, 2019
- A Large-Scale Empirical Study on Industrial Fake Apps2019
- Mobile Application Impersonation Detection Using Dynamic User Interface Extraction2016
- Finding a "Kneedle" in a Haystack: Detecting Knee Points in System Behavior2011