  We present a data-driven algorithm to model and predict the socio-emotional
impact of groups on observers. Psychological research finds that highly
entitative i.e. cohesive and uniform groups induce threat and unease in
observers. Our algorithm models realistic trajectory-level behaviors to
classify and map the motion-based entitativity of crowds. This mapping is based
on a statistical scheme that dynamically learns pedestrian behavior and
computes the resultant entitativity induced emotion through group motion
characteristics. We also present a novel interactive multi-agent simulation
algorithm to model entitative groups and conduct a VR user study to validate
the socio-emotional predictive power of our algorithm. We further show that
model-generated high-entitativity groups do induce more negative emotions than
low-entitative groups.
