  Video game streaming provides the viewer with a rich set of audio-visual
data, conveying information both with regards to the game itself, through game
footage and audio, as well as the streamer's emotional state and behaviour via
webcam footage and audio. Analysing player behaviour and discovering
correlations with game context is crucial for modelling and understanding
important aspects of livestreams, but comes with a significant set of
challenges - such as fusing multimodal data captured by different sensors in
uncontrolled ('in-the-wild') conditions. Firstly, we present, to our knowledge,
the first data set of League of Legends livestreams, annotated for both
streamer affect and game context. Secondly, we propose a method that exploits
tensor decompositions for high-order fusion of multimodal representations. The
proposed method is evaluated on the problem of jointly predicting game context
and player affect, compared with a set of baseline fusion approaches such as
late and early fusion.
