Human Interaction and Networking Transitions System (HINTS) for Social User Analytics and Modeling of Offline Team Group Interaction Information
Authors: 
Ngoc Doan, Dan Hudson, Travis J. Wiltshire, Philia Lijdsman, Stijn Wever, Martin AtzmuellerAuthors Info & Claims
Ngoc Doan, Dan Hudson, Travis J. Wiltshire, Philia Lijdsman, Stijn Wever, Martin AtzmuellerAuthors Info & ClaimsPages 71 - 74
Supplementary Material
We present HINTS (Human Interaction and Networking Transitions System), a novel system for visualising the dynamics of face-to-face collaboration. Our method relies on data gathered through ?sociometric badges?, i. e., unobtrusive wearable sensors that allow for an impartial and detailed investigation of team interactions over the full duration of an event. The captured data is then analyzed with a novel combination of measures based on dynamic complexity and turn-taking behaviours. The analysis reveal the temporal structure in team interaction data, which is then visualized in a easily interpretable manner. The visualisations should give a clear message to users from all background, even who do not have a technical background in the topic of user modeling and social analytics in general. The system has applications in a wide range of context, both academic and professional, as a tool for capturing and analysing rich data about human interactions in a comprehensible way.
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References
[1]
Martin Atzmueller. 2014. Analyzing and Grounding Social Interaction in Online and Offline Networks. In Proc. ECML/PKDD(LNCS, Vol. 8726). Springer, Berlin, Germany, 485–488.
[2]
Martin Atzmueller. 2018. Compositional Subgroup Discovery on Attributed Social Interaction Networks. In Proc. Discovery Science(LNCS, Vol. 11198). Springer, Berlin/Heidelberg, Germany, 259–275.
[3]
Martin Atzmueller, Martin Becker, Mark Kibanov, Christoph Scholz, Stephan Doerfel, Andreas Hotho, Bjoern-Elmar Macek, Folke Mitzlaff, Juergen Mueller, and Gerd Stumme. 2014. Ubicon and its Applications for Ubiquitous Social Computing. New Review of Hypermedia and Multimedia 20, 1 (2014), 53–77. https://doi.org/10.1080/13614568.2013.873488
[4]
Martin Atzmueller, Andreas Ernst, Friedrich Krebs, Christoph Scholz, and Gerd Stumme. 2014. On the Evolution of Social Groups During Coffee Breaks. In Proc. WWW 2014 (Companion). IW3C2 / ACM.
[5]
Martin Atzmueller, Lisa Thiele, Gerd Stumme, and Simone Kauffeld. 2016. Analyzing Group Interaction and Dynamics on Socio-Behavioral Networks of Face-to-Face Proximity. In Proc. ACM Conference on Pervasive and Ubiquitous Computing(UbiComp ’16 Adjunct). ACM Press, New York, NY, USA.
[6]
Alain Barrat, Ciro Cattuto, Vittoria Colizza, Jean-François Pinton, Wouter Van den Broeck, and Alessandro Vespignani. 2010. High Resolution Dynamical Mapping of Social Interactions with Active RFID. PLoS ONE 5, 7 (2010), e11596.
[7]
Carolina Centeio Jorge, Martin Atzmueller, Behzad M Heravi, Jenny L Gibson, Rosaldo JF Rossetti, and Cláudio Rebelo de Sá. 2021. “Want to Come Play with me?” Outlier Subgroup Discovery on Spatio-Temporal Interactions. Expert Systems (2021).
[8]
Julius Dietzel, ER Francu, Benjamin Lucas, and Mohamed Zaki. 2018. Contextually Defined Postural Markers Reveal Who’s in Charge: Evidence from Small Teams Collected with Wearable Sensors.
[9]
Ngoc N. T. Doan, Andrius Penkauskas, Ecaterina Grigoriev, Lisa E. Rombout, Paris Mavromoustakos-Blom, Maryam Alimardani, and Martin Atzmueller. 2019. Towards Multimodal Characterization of Dialogic Moments On Social Group Face-to-Face Interaction. In Proc. Workshop on Affective Computing and Context Awareness in Ambient Intelligence. UPCT, Cartagena, Spain.
[10]
Stephen M Fiore, Michael A Rosen, Kimberly A Smith-Jentsch, Eduardo Salas, Michael Letsky, and Norman Warner. 2010. Toward an understanding of macrocognition in teams: Predicting processes in complex collaborative contexts. Human Factors 52, 2 (2010), 203–224.
[11]
Peter A Gloor, Francesca Grippa, Johannes Putzke, Casper Lassenius, Hauke Fuehres, Kai Fischbach, and Detlef Schoder. 2012. Measuring social capital in creative teams through sociometric sensors. International Journal of Organisational Design and Engineering 2, 4(2012), 380–401.
[12]
JAS Kelso, John P Scholz, and Gregor Schöner. 1986. Nonequilibrium phase transitions in coordinated biological motion: critical fluctuations. Physics Letters A 118, 6 (1986), 279–284.
[13]
Mark Kibanov, Martin Atzmueller, Jens Illig, Christoph Scholz, Alain Barrat, Ciro Cattuto, and Gerd Stumme. 2015. Is Web Content a Good Proxy for Real-Life Interaction? A Case Study Considering Online and Offline Interactions of Computer Scientists. In Proc. IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE Press, Boston, MA, USA.
[14]
Mark Kibanov, Raphael Heiberger, Simone Roedder, Martin Atzmueller, and Gerd Stumme. 2019. Social Studies of Scholarly Live with Sensor-based Ethnographic Observations. Scientometrics 119, 3 (2019), 1387–1428.
[15]
Joanne Kitchin and Chris Baber. 2017. The dynamics of distributed situation awareness. In Proceedings of the human factors and ergonomics society annual meeting, Vol. 61. SAGE Publications Sage CA: Los Angeles, CA, 277–281.
[16]
Steve WJ Kozlowski and Georgia T Chao. 2018. Unpacking team process dynamics and emergent phenomena: Challenges, conceptual advances, and innovative methods.American Psychologist 73, 4 (2018), 576.
[17]
Oren Lederman, Akshay Mohan, Dan Calacci, and Alex Sandy Pentland. 2018. Rhythm: A unified measurement platform for human organizations. IEEE MultiMedia 25, 1 (2018), 26–38.
[18]
Bjoern-Elmar Macek, Martin Atzmueller, and Gerd Stumme. 2011. Profile Mining in CVS-Logs and Face-to-Face Contacts for Recommending Software Developers. In Proc. IEEE Third International Conference on Social Computing (SocialCom). IEEE Computer Society, Boston, MA, USA, 250–257.
[19]
Shannon L Marlow, Christina N Lacerenza, Jensine Paoletti, C Shawn Burke, and Eduardo Salas. 2018. Does team communication represent a one-size-fits-all approach?: A meta-analysis of team communication and performance. Organizational Behavior and Human Decision Processes 144 (2018), 145–170.
[20]
Christopher McComb, Jonathan Cagan, and Kenneth Kotovsky. 2014. Quantitative comparison of high-and low-performing teams in a design task subject to drastic changes. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Vol. 46407. American Society of Mechanical Engineers, V007T07A033.
[21]
Rosalie J Ocker and Jerry Fjermestad. 2008. Communication differences in virtual design teams: findings from a multi-method analysis of high and low performing experimental teams. ACM SIGMIS Database: the DATABASE for Advances in Information Systems 39, 1(2008), 51–67.
[22]
Alex Sandy Pentland. 2012. The new science of building great teams. Harv.Bus.Rev. 90, 4 (2012).
[23]
Günter Schiepek and Guido Strunk. 2010. The identification of critical fluctuations and phase transitions in short term and coarse-grained time series—a method for the real-time monitoring of human change processes. Biological cybernetics 102, 3 (2010), 197–207.
[24]
Christoph Scholz, Martin Atzmueller, Mark Kibanov, and Gerd Stumme. 2013. How Do People Link? Analysis of Contact Structures in Human Face-to-Face Proximity Networks. In Proc. ASONAM 2013. ACM Press, New York, NY, USA.
[25]
Christoph Scholz, Martin Atzmueller, and Gerd Stumme. 2012. On the Predictability of Human Contacts: Influence Factors and the Strength of Stronger Ties. In Proc. 4th ASE/IEEE International Conference on Social Computing (SocialCom). IEEE Computer Society, Boston, MA, USA.
[26]
Travis J Wiltshire, Jonathan E Butner, and Stephen M Fiore. 2018. Problem-solving phase transitions during team collaboration. Cognitive science 42, 1 (2018), 129–167.
[27]
Travis J. Wiltshire, Dan Hudson, Philia Lijdsman, Stijn Wever, and Martin Atzmueller. 2020. Social Analytics of Team Interaction using Dynamic Complexity Heat Maps and Network Visualizations. CoRR abs/2009.04445(2020). https://bit.ly/32cNC73
[28]
Travis J Wiltshire, Sune Vork Steffensen, and Stephen M Fiore. 2019. Multiscale movement coordination dynamics in collaborative team problem solving. Applied ergonomics 79(2019), 143–151.
[29]
Yanxia Zhang, Jeffrey Olenick, Chu-Hsiang Chang, Steve WJ Kozlowski, and Hayley Hung. 2018. TeamSense: assessing personal affect and group cohesion in small teams through dyadic interaction and behavior analysis with wearable sensors. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 3 (2018), 1–22.
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Published In
June 2021
431 pages
ISBN:9781450383677
DOI:10.1145/3450614
Copyright © 2021 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Published: 22 June 2021
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UMAP '21: 29th ACM Conference on User Modeling, Adaptation and Personalization
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