  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.
