Understanding user activity patterns of the Swarm app

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.