The I in Team
Mining Personal Social Interaction Routine with Topic Models from Long-Term Team Data
Yanxia Zhang (TU Delft - Pattern Recognition and Bioinformatics)
Jeffrey Olenick (Michigan State University)
Chu-Hsiang Chang (Michigan State University)
Steve W. J. Kozlowski (Michigan State University)
H.S. Hung (TU Delft - Pattern Recognition and Bioinformatics)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Social interaction plays a key role in assessing teamwork and collaboration. It becomes particularly critical in team performance when coupled with isolated, confined, and extreme conditions such as undersea missions. This work investigates how social interactions of individual members in a small team evolve during the course of a long duration mission. We propose to use a topic model to mine individual social interaction patterns and examine how the dynamics of these patterns have an effect on self-assessment of mood and team cohesion. Specifically, we analyzed data from a 6-person crew wearing Sociometric badges over a 4-month mission. Our results show that our method can extract the latent structure of social contexts without supervision. We demonstrate how the extracted patterns based on probabilistic models can provide insights on common behaviors at various temporal resolutions and exhibit links with self-report affective states and team cohesion.