Reducing Videoconferencing Fatigue through Facial Emotion Recognition

Jannik Rößler, Jiachen Sun and Peter Gloor
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Jannik Rößler: Cologne Institute for Information Systems, University of Cologne, 50923 Cologne, Germany
Jiachen Sun: School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
Peter Gloor: MIT Center for Collective Intelligence, Massachusetts Institute of Technology, Cambridge, MA 02142, USA

Future Internet, 2021, vol. 13, issue 5, 1-15

Abstract: In the last 14 months, COVID-19 made face-to-face meetings impossible and this has led to rapid growth in videoconferencing. As highly social creatures, humans strive for direct interpersonal interaction, which means that in most of these video meetings the webcam is switched on and people are “looking each other in the eyes”. However, it is far from clear what the psychological consequences of this shift to virtual face-to-face communication are and if there are methods to alleviate “videoconferencing fatigue”. We have studied the influence of emotions of meeting participants on the perceived outcome of video meetings. Our experimental setting consisted of 35 participants collaborating in eight teams over Zoom in a one semester course on Collaborative Innovation Networks in bi-weekly video meetings, where each team presented its progress. Emotion was tracked through Zoom face video snapshots using facial emotion recognition that recognized six emotions (happy, sad, fear, anger, neutral, and surprise). Our dependent variable was a score given after each presentation by all participants except the presenter. We found that the happier the speaker is, the happier and less neutral the audience is. More importantly, we found that the presentations that triggered wide swings in “fear” and “joy” among the participants are correlated with a higher rating. Our findings provide valuable input for online video presenters on how to conduct better and less tiring meetings; this will lead to a decrease in “videoconferencing fatigue”.

Keywords: facial emotion recognition; social network analysis; video meetings (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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