# Discovering activity transition patterns in social media check-in behavior via temporal activity motifs

## TL;DR

This paper analyzes social media check-in data to uncover patterns in how people transition between different types of activities, revealing insights for better location-based services.

## Contribution

The paper introduces temporal activity motifs to identify frequent activity type transitions in check-in data.

## Key findings

- 383 temporal activity motifs were extracted from the Gowalla dataset, revealing recurring activity patterns.
- Activity types show varying influences and positions within processes, highlighting cyclical human mobility.
- Non-sequential motifs reveal co-occurrence patterns between separate activity processes.

## Abstract

Location-Based Social Network (LBSN) has produced a large quantity of user check-in data. A profound understanding of user behavior and intrinsic needs can be achieved by identifying patterns in activity type transitions, thereby enabling more intelligent location-based services. We proposed temporal activity motif and used this structure to identify frequent activity type transition patterns from check-in sequences, discovering the relationship and interaction between different activity types. 383 temporal activity motifs of 17 temporal topologies were extracted from the two-year Gowalla dataset of New York-Newark-Jersey City, NY-NJ-PA Metropolitan Statistical Area (MSA). These motifs are categorized into two groups: one is sequential motifs representing a complete activity process, while the other is non-sequential motifs representing the co-occurrence of two separate processes. They provide evidence of activity type recurrence, particularly in longer activity processes, highlights the cyclical nature of human mobility. Additionally, various activity types exhibit different influences on others and occupy different positions in activity processes. Furthermore, by leveraging non-sequential motifs, we specifically uncovered the co-occurrence patterns between two separate activity process. These findings bring new insights to optimize recommendation system and urban planning.

The online version contains supplementary material available at 10.1038/s41598-025-14843-x.