GameVibe: A Multimodal Affective Game Corpus

Published in Arxiv, 2024

Recommended citation: Barthet, M., Kaselimi, M., Pinitas, K., Makantasis, K., Liapis, A., & Yannakakis, G. N. (2024). GameVibe: A Multimodal Affective Game Corpus. arXiv preprint arXiv:2407.12787.

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 labels 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.

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