Understanding user activity patterns of the Swarm app

Shihan Lin · Rong Xie · Qinge Xie · Hao Zhao · Yang Chen

14 citations0/0 files storedACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp)

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Human Mobility and Location-Based Analysis Context-Aware Activity Recognition Systems Data Management and Algorithms

Abstract

Location-based social apps have been widely used by people to share their location information with friends. These apps provide rich spatial-temporal information for researchers to investigate user activity patterns. In this work, we collect check-in data from Swarm, and analyze the user behavior in a way of combining spatial and temporal features of check-ins. The results reveal users' different preferences for venue categories in different time of the day. Our work presents activity patterns of human behavior and the distinctions of life habits among three cities, Hong Kong, New York City, and San Francisco. Our findings can be further applied to Swarm's incentive mechanism and recommendation systems.

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References

2021Exploring Millions of Footprints in Location Sharing Services 2013Mining user behaviours 2016Modeling Urban Behavior by Mining Geotagged Social Data

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2020A Study of User Activity Patterns and the Effect of Venue Types on City Dynamics Using Location-Based Social Network Data 2019GeoLifecycle 2020Location-Based Social Network’s Data Analysis and Spatio-Temporal Modeling for the Mega City of Shanghai, China 2018Understanding Skout users’ mobility patterns on a global scale: a data-driven study 2023Digital Participatory Model as Part of a Data-Driven Decision Support System for Urban Vibrancy

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