The I in Team: Mining Personal Social Interaction Routine with Topic Models from Long-Term Team Data

Yanxia Zhang, Jeffrey Olenick, Chu-Hsiang Chang, Steve W.J. Kozlowski, Hayley Hung

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

5 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationIUI 2018 - Proceedings of the 23rd International Conference on Intelligent User Interfaces
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery (ACM)
Pages421-426
Number of pages6
ISBN (Electronic)978-1-4503-4945-1
DOIs
Publication statusPublished - 2018
EventIUI 2018: 23rd International Conference on Intelligent User Intefaces - Tokyo, Japan
Duration: 7 Mar 201811 Mar 2018
Conference number: 23
http://iui.acm.org/2018/

Conference

ConferenceIUI 2018
Abbreviated titleIUI'18
CountryJapan
CityTokyo
Period7/03/1811/03/18
Internet address

Keywords

  • Machine learning
  • Team dynamics
  • Wearable

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  • Cite this

    Zhang, Y., Olenick, J., Chang, C-H., Kozlowski, S. W. J., & Hung, H. (2018). The I in Team: Mining Personal Social Interaction Routine with Topic Models from Long-Term Team Data. In IUI 2018 - Proceedings of the 23rd International Conference on Intelligent User Interfaces (pp. 421-426). Association for Computing Machinery (ACM). https://doi.org/10.1145/3172944.3172997