  The problem of understanding people's participation in real-world events has
been a subject of active research and can offer valuable insights for human
behavior analysis and event-related recommendation/advertisement. In this work,
we study the latent factors for determining event popularity using large-scale
datasets collected from the popular Meetup.com EBSN in three major cities
around the world. We have conducted modeling analysis of four contextual
factors (spatial, group, temporal, and semantic), and also developed a
group-based social influence propagation network to model group-specific
influences on events. By combining the Contextual features And Social Influence
NetwOrk, our integrated prediction framework CASINO can capture the diverse
influential factors of event participation and can be used by event organizers
to predict/improve the popularity of their events. Evaluations demonstrate that
our CASINO framework achieves high prediction accuracy with contributions from
all the latent features we capture.
