GameVibe: a multimodal affective game corpus

As online video and streaming platforms continue to grow, affective computing research has undergone a shift towards more complex studies involving multiple modalities. However, there is still a lack of readily available datasets with high-quality audiovisual stimuli. In this paper, we present GameV...

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Bibliographic Details
Published in:Scientific data 2024-11, Vol.11 (1), p.1306-10
Main Authors: Barthet, Matthew, Kaselimi, Maria, Pinitas, Kosmas, Makantasis, Konstantinos, Liapis, Antonios, Yannakakis, Georgios N.
Format: Article
Language:English
Subjects:
706/648/697/129
706/689/680
Affect
Annotations
Data collection
Data Descriptor
Datasets
Design
Games
Genre
Humanities and Social Sciences
Humans
multidisciplinary
Quality control
Science
Science (multidisciplinary)
Sensory integration
Streaming
Streaming media
User behavior
Video Games
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Summary:As online video and streaming platforms continue to grow, affective computing research has undergone a shift towards more complex studies involving multiple modalities. However, there is still a lack of readily available datasets with high-quality audiovisual stimuli. In this paper, we present GameVibe, a novel affect corpus which consists of multimodal audiovisual stimuli, including in-game behavioural observations and third-person affect traces for viewer engagement. The corpus consists of videos from a diverse set of publicly available gameplay sessions across 30 games, with particular attention to ensure high-quality stimuli with good audiovisual and gameplay diversity. Furthermore, we present an analysis on the reliability of the annotators in terms of inter-annotator agreement.
ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-024-04022-4