"What Apps Did You Use?": Understanding the Long-term Evolution of Mobile App Usage
Tong Li, Mingyang Zhang, Hancheng Cao, Yong Li, Sasu Tarkoma, Pan Hui
Abstract
The prevalence of smartphones has promoted the popularity of mobile apps in recent years. Although significant effort has been made to understand mobile app usage, existing studies are based primarily on short-term datasets with limited time span, e.g., a few months. Therefore, many basic facts about the long-term evolution of mobile app usage are unknown. In this paper, we study how mobile app usage evolves over a long-term period. We first introduce an app usage collection platform named carat, from which we have gathered app usage records of 1,465 users from 2012 to 2017. We then conduct the first study on the long-term evolution processes on a macro-level, i.e., app-category, and micro-level, i.e., individual app. We discover that, on both levels, there is a growth stage enabled by the introduction of new technologies. Then there is a plateau stage caused by high correlations between app categories and a pareto effect in individual app usage, respectively. Additionally, the evolution of individual app usage undergoes an elimination stage due to fierce intra-category competition. Nevertheless, the diverseness of app-category and individual app usage exhibit opposing trends: app-category usage assimilates while individual app usage diversifies. Our study provides useful implications for app developers, market intermediaries, and service providers. CCS CONCEPTS • Human-centered computing → Empirical studies in ubiquitous and mobile computing.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 060b3307-a145-49ca-9200-7c2f8e0be668Cited by top-tier papers4
- Semantic-aware Spatio-temporal App Usage Representation via Graph Convolutional NetworkYue Yu, Tong Xia, Huandong Wang, Jie Feng et al.UbiComp 2020 · 28 citations
- Are You Killing Time? Predicting Smartphone Users' Time-killing Moments via Fusion of Smartphone Sensor Data and ScreenshotsYu-Chun Chen, Yu-Jen Lee, Kuei-Chun Kao, Jie Tsai et al.CHI 2023 · 14 citations
- Practitioners Versus Users: A Value-Sensitive Evaluation of Current Industrial Recommender System DesignZhilong Chen, Jinghua Piao, Xiaochong Lan, Hancheng Cao et al.CSCW 2022 · 12 citations
- AllHands :Ask Me Anything on Large-scale Verbatim Feedback via Large Language ModelsChaoyun Zhang, Zicheng Ma, Yuhao Wu, Shilin He et al.ICDE 2025 · 3 citations
Related papers
- Impact of Later-Stages COVID-19 Response Measures on Spatiotemporal Mobile Service UsageAndré Felipe Zanella, Orlando Martínez-Durive, Sachit Mishra, Zbigniew Smoreda et al.INFOCOM 2022 · 8 citations
- An Urban Geography of Mobile Application Usage: Connecting Demand Dynamics and Urban FabricsSachit Mishra, Diego Madariaga, Cezary Ziemlicki, Diala Naboulsi et al.INFOCOM 2025 · 3 citations
- A Longitudinal In-the-Wild Investigation of Design Frictions to Prevent Smartphone OveruseLuke Haliburton, David Joachim Grüning, Frederik Riedel, Albrecht Schmidt et al.CHI 2024 · 23 citations
- Second-level Digital Divide: A Longitudinal Study of Mobile Traffic Consumption Imbalance in FranceSachit Mishra, Zbigniew Smoreda, Marco FioreWWW 2022 · 11 citations
- Understanding Adoption, Use, and Abandonment Practices in Baby TrackingAlexandra Papoutsaki, Mustafa Taha Disbudak, Lily Galvan, Chau Vu et al.CHI 2026 · 2 citations
