GameVibe: A Multimodal Affective Game Corpus
<|reference_start|>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 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.<|reference_end|>
@article{barthet2024gamevibe:,
    title={GameVibe: A Multimodal Affective Game Corpus},
    author={Matthew Barthet, Maria Kaselimi, Kosmas Pinitas, Konstantinos
  Makantasis, Antonios Liapis, Georgios N. Yannakakis},
    journal={arXiv preprint arXiv:2407.12787},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.12787},
    primaryClass={cs.HC cs.AI}
}