GameVibe: A Multimodal Affective Game Corpus
This provides a new dataset for researchers in affective computing, but it is incremental as it addresses a known data gap without solving a broader problem.
The authors tackled the lack of high-quality multimodal datasets for affective computing by introducing GameVibe, a corpus of audiovisual gameplay videos from 30 games, and they reported results on annotator reliability through inter-annotator agreement analysis.
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.