A Tale of Two Countries: A Longitudinal Cross-Country Study of Mobile Users' Reactions to the COVID-19 Pandemic Through the Lens of App Popularity

The ongoing COVID-19 pandemic has profoundly impacted people’s life around the world, including how they interact with mobile technologies. In this paper, we seek to develop an understanding of how the dynamic trajectory of a pandemic shapes mobile phone users’ experiences. Through the lens of app popularity, we approach this goal from a cross-country perspective. We compile a dataset consisting of six-month daily snapshots of the most popular apps in the iOS App Store in China and the US, where the pandemic has exhibited distinct trajectories. Using this longitudinal dataset, our analysis provides detailed patterns of app ranking during the pandemic at both category and individual app levels. We reveal that app categories’ rankings are correlated with the pandemic, contingent upon country-specific development trajectories. Our work offers rich insights into how the COVID-19, a typical global public health crisis, has influence people’s day-to-day interaction with the Internet and mobile technologies.

[1]  Yan Bai,et al.  Presumed Asymptomatic Carrier Transmission of COVID-19. , 2020, JAMA.

[2]  A. Büssing,et al.  Tumor Patients´ Perceived Changes of Specific Attitudes, Perceptions, and Behaviors Due to the COVID-19 Pandemic and Its Relation to Reduced Wellbeing , 2020, Frontiers in Psychiatry.

[3]  Li Li,et al.  Why are Android Apps Removed From Google Play? A Large-Scale Empirical Study , 2018, 2018 IEEE/ACM 15th International Conference on Mining Software Repositories (MSR).

[4]  Hao Li,et al.  Understanding the Evolution of Mobile App Ecosystems: A Longitudinal Measurement Study of Google Play , 2019, WWW.

[5]  Nourah Altakarli China’s Response to the COVID-19 Outbreak: A Model for Epidemic Preparedness and Management , 2020, Dubai Medical Journal.

[6]  Walid Maalej,et al.  User feedback in the appstore: An empirical study , 2013, 2013 21st IEEE International Requirements Engineering Conference (RE).

[7]  Haoyu Wang,et al.  Using text mining to infer the purpose of permission use in mobile apps , 2015, UbiComp.

[8]  Amit Kramer,et al.  The potential impact of the Covid-19 pandemic on occupational status, work from home, and occupational mobility , 2020, Journal of Vocational Behavior.

[9]  Minhui Xue,et al.  An Empirical Assessment of Global COVID-19 Contact Tracing Applications , 2021, 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE).

[10]  Haoyu Wang,et al.  Identifying and Analyzing the Privacy of Apps for Kids , 2016, HotMobile.

[11]  S. Lindstrom,et al.  First Case of 2019 Novel Coronavirus in the United States , 2020, The New England journal of medicine.

[12]  Yao Guo,et al.  DaPanda: Detecting Aggressive Push Notifications in Android Apps , 2019, 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE).

[13]  Narseo Vallina-Rodriguez,et al.  Beyond Google Play: A Large-Scale Comparative Study of Chinese Android App Markets , 2018, Internet Measurement Conference.

[14]  Haoyu Wang,et al.  Reevaluating Android Permission Gaps with Static and Dynamic Analysis , 2014, 2015 IEEE Global Communications Conference (GLOBECOM).

[15]  Haoyu Wang,et al.  Understanding Third-Party Libraries in Mobile App Analysis , 2017, 2017 IEEE/ACM 39th International Conference on Software Engineering Companion (ICSE-C).

[16]  J. Crowcroft,et al.  Leveraging Data Science to Combat COVID-19: A Comprehensive Review , 2020, IEEE Transactions on Artificial Intelligence.

[17]  Vahid Garousi,et al.  Mining user reviews of COVID contact-tracing apps: An exploratory analysis of nine European apps , 2020, Journal of Systems and Software.

[18]  Lei Wu,et al.  Mobile App Squatting , 2020, WWW.

[19]  Jacques Klein,et al.  On Identifying and Explaining Similarities in Android Apps , 2019, Journal of Computer Science and Technology.

[20]  Yajin Zhou,et al.  Demystifying Diehard Android Apps , 2020, 2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE).

[21]  Jacques Klein,et al.  FraudDroid: automated ad fraud detection for Android apps , 2017, ESEC/SIGSOFT FSE.

[22]  Haoyu Wang,et al.  LibRadar: Fast and Accurate Detection of Third-Party Libraries in Android Apps , 2016, 2016 IEEE/ACM 38th International Conference on Software Engineering Companion (ICSE-C).

[23]  Long Jiang Zhang,et al.  Coronavirus Disease 2019 (COVID-19): A Perspective from China , 2020, Radiology.

[24]  G. Onder,et al.  Case-Fatality Rate and Characteristics of Patients Dying in Relation to COVID-19 in Italy. , 2020, JAMA.

[25]  Li Li,et al.  Dating with Scambots: Understanding the Ecosystem of Fraudulent Dating Applications , 2018, IEEE Transactions on Dependable and Secure Computing.

[26]  Kristina Winbladh,et al.  Analysis of user comments: An approach for software requirements evolution , 2013, 2013 35th International Conference on Software Engineering (ICSE).

[27]  Haoyu Wang,et al.  An Explorative Study of the Mobile App Ecosystem from App Developers' Perspective , 2017, WWW.

[28]  Yajin Zhou,et al.  Dissecting Android Malware: Characterization and Evolution , 2012, 2012 IEEE Symposium on Security and Privacy.

[29]  Narseo Vallina-Rodriguez,et al.  Apps, Trackers, Privacy, and Regulators: A Global Study of the Mobile Tracking Ecosystem , 2018, NDSS.

[30]  Alex Pentland,et al.  Mobile phone data and COVID-19: Missing an opportunity? , 2020, ArXiv.

[31]  Haoyu Wang,et al.  Characterizing the Global Mobile App Developers: A Large-Scale Empirical Study , 2019, 2019 IEEE/ACM 6th International Conference on Mobile Software Engineering and Systems (MOBILESoft).

[32]  Li Li,et al.  How do Mobile Apps Violate the Behavioral Policy of Advertisement Libraries? , 2018, HotMobile '18.

[33]  Guozhu Meng,et al.  Characterizing Android App Signing Issues , 2019, 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE).

[34]  Alexander Wong,et al.  COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images , 2020, Scientific reports.

[35]  Haoyu Wang,et al.  A3Ident: A Two-phased Approach to Identify the Leading Authors of Android Apps , 2020, 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME).

[36]  Helge Janicke,et al.  A Survey of COVID-19 Contact Tracing Apps , 2020, IEEE Access.

[37]  Narseo Vallina-Rodriguez,et al.  50 Ways to Leak Your Data: An Exploration of Apps' Circumvention of the Android Permissions System , 2019, USENIX Security Symposium.

[38]  Chunhua Shen,et al.  COVID-19 Screening on Chest X-ray Images Using Deep Learning based Anomaly Detection , 2020, ArXiv.

[39]  J. Evans Straightforward Statistics for the Behavioral Sciences , 1995 .

[40]  Haoyu Wang,et al.  All your app links are belong to us: understanding the threats of instant apps based attacks , 2020, ESEC/SIGSOFT FSE.

[41]  Li Li,et al.  Automated Third-Party Library Detection for Android Applications: Are We There Yet? , 2020, 2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE).

[42]  Ping Zhong,et al.  Correlation between travellers departing from Wuhan before the Spring Festival and subsequent spread of COVID-19 to all provinces in China , 2020, Journal of travel medicine.

[43]  Xiapu Luo,et al.  Beyond the Virus: A First Look at Coronavirus-themed Mobile Malware , 2020, ArXiv.

[44]  Jing Yuan,et al.  Treatment of 5 Critically Ill Patients With COVID-19 With Convalescent Plasma. , 2020, JAMA.

[45]  Li Li,et al.  Dissecting Mobile Offerwall Advertisements: An Explorative Study , 2020, 2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS).

[46]  Jacques Klein,et al.  MadDroid: Characterising and Detecting Devious Ad Content for Android Apps , 2020, ArXiv.

[47]  Fengyuan Xu,et al.  DeepIntent: Deep Icon-Behavior Learning for Detecting Intention-Behavior Discrepancy in Mobile Apps , 2019, CCS.

[48]  Masooda N. Bashir,et al.  Advocating for Users’ Privacy Protections: A Case study of COVID-19 apps , 2020, MobileHCI.

[49]  Hyunghoon Cho,et al.  Contact Tracing Mobile Apps for COVID-19: Privacy Considerations and Related Trade-offs , 2020, ArXiv.

[50]  Huiyi Wang,et al.  Market-level Analysis of Government-backed COVID-19 Contact Tracing Apps , 2020, 2020 35th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW).

[51]  Banita Lal,et al.  Impact of COVID-19 pandemic on information management research and practice: Transforming education, work and life , 2020, Int. J. Inf. Manag..

[52]  Jacques Klein,et al.  Rebooting Research on Detecting Repackaged Android Apps: Literature Review and Benchmark , 2018, IEEE Transactions on Software Engineering.

[53]  Hao Li,et al.  RmvDroid: Towards A Reliable Android Malware Dataset with App Metadata , 2019, 2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR).