Publication Type

Journal Article

Version

publishedVersion

Publication Date

4-2024

Abstract

Human social interactions occur in group settings of varying sizes and locations, depending on the type of social activity. The ability to distinguish group formations based on their purposes transforms how group detection mechanisms function. Not only should such tools support the effective detection of serendipitous encounters, but they can derive categories of relation types among users. Determining who is involved, what activity is performed, and when and where the activity occurs are critical to understanding group processes in greater depth, including supporting goal-oriented applications (e.g., performance, productivity, and mental health) that require sensing social factors. In this work, we propose W4-Groups that captures the functional perspective of variability and repeatability when automatically constructing short-term and long-term groups via multiple data sources (e.g., WiFi and location check-in data). We design and implement W4-Groups to detect and extract all four group features who-what-when-where from the user's daily mobility patterns. We empirically evaluate the framework using two real-world WiFi datasets and a location check-in dataset, yielding an average of 92% overall accuracy, 96% precision, and 94% recall. Further, we supplement two case studies to demonstrate the application of W4-Groups for next-group activity prediction and analyzing changes in group behavior at a longitudinal scale, exemplifying short-term and long-term occurrences.

Keywords

group modeling, next activity prediction, social interactions, user mobility

Discipline

Interpersonal and Small Group Communication | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the ACM on Human-Computer Interaction

Volume

8

First Page

1

Last Page

29

ISSN

2573-0142

Identifier

10.1145/3637427

Publisher

Association for Computing Machinery (ACM)

Copyright Owner and License

Authors CC-BY

Additional URL

https://doi.org/10.1145/3637427

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