Demographics of mobile app usage: long-term analysis of mobile app usage

In the past decade, mobile app usage has played an important role in our daily life. Existing studies have shown that app usage is intrinsically linked with, among others, demographics, social and economic factors. However, due to data limitations, most of these studies have a short time span and treat users in a static manner. To date, no study has shown whether changes in socioeconomic status or other demographics are reflected in long-term app usage behavior. In this paper, we contribute by presenting the first ever long-term study of individual mobile app usage dynamics and how app usage behavior of individuals is influenced by changes in socioeconomic demographic factors over time. Through a novel app dataset we collected, from which we extracted records of 1608 long-term users with more than 3-year app usage and their detailed socioeconomic attributes, we verify the stable correlation between user app usage and user socioeconomic attributes over time and identify a number of representative app usage patterns in connection with specific user attributes. On the basis, we analyze the long-term app usage dynamics and reveal that there is significant evolution in long-term app usage that 60–70% of users change their app usage patterns during the duration of more than 3 years. We further discover a variety of app pattern change modes and demonstrate that the long-term app usage behavior change reflects corresponding transition in socioeconomic attributes, such as change of civil status, family size, transition in job or economic status.

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Authors and Affiliations

1. State Key Laboratory on Microwave and Digital Communications, Tsinghua National Laboratory for Information Science and Technology, Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China Zhen Tu & Yong Li
2. Department of Computer Science, University of Helsinki, Helsinki, Finland Eemil Lagerspetz, Huber Flores, Sasu Tarkoma & Petteri Nurmi
3. Department of Computer Science, Stanford University, Stanford, CA, USA Hancheng Cao
4. Department of Sun Yat-Sen University, Guangzhou, China Yali Fan

1. Zhen Tu