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Paper Number
1081
Paper Type
Completed
Description
Metaverses are immersive virtual worlds in which people interact as avatars. There is emerging interest in understanding how metaverse users behave and perceive activities and tasks. Our understanding of users’ behavior within metaverses is limited. This study examines the role of emotions in the movement of individuals. We therefore implement a metaverse setting using virtual reality technology and development tools. In our study, we manipulated negative emotions and tracked the movements of our participants. We show how negative emotion influences movements in a metaverse setting. Based on a literature review, we select and calculate movement features to train a support vector machine. As our result, we present a novel way to infer the negative emotions of metaverse users which will help create more engaging and immersive experiences that cater to user’s emotions and behaviors. Our study provides preliminary evidence for the potential utilization of movement data in the metaverse.
Recommended Citation
Menck, Jannes Heinrich Diedrich; Uschakov, Evgeni; Lechte, Henrik; Lembcke, Tim-Benjamin; and Kolbe, Lutz M., "How are you Feeling? Inferring Emotions through Movements in the Metaverse" (2023). ICIS 2023 Proceedings. 1.
https://aisel.aisnet.org/icis2023/dab_sc/dab_sc/1
How are you Feeling? Inferring Emotions through Movements in the Metaverse
Metaverses are immersive virtual worlds in which people interact as avatars. There is emerging interest in understanding how metaverse users behave and perceive activities and tasks. Our understanding of users’ behavior within metaverses is limited. This study examines the role of emotions in the movement of individuals. We therefore implement a metaverse setting using virtual reality technology and development tools. In our study, we manipulated negative emotions and tracked the movements of our participants. We show how negative emotion influences movements in a metaverse setting. Based on a literature review, we select and calculate movement features to train a support vector machine. As our result, we present a novel way to infer the negative emotions of metaverse users which will help create more engaging and immersive experiences that cater to user’s emotions and behaviors. Our study provides preliminary evidence for the potential utilization of movement data in the metaverse.
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13-DataAnalytics