Toward estimating user-social event distance: mobility, content, and social relationship

On-site user w.r.t social events are valuable, from whom, government/police could obtain meaningful information which contributes to understand the progress of the event or investigate suspects when the event is associated with crime or terrorist. However, due to the high uncertainty of human mobility patterns, it is hard to identify on-site users while social event happens. In this paper, we propose a F>used fEature Gaussian prOcess Rgression (FEGOR) model, which employs three features from online social networks: mobility influence, content similarity, and social relationship to estimate the distance between user and social event, based on which, we could accomplish the problem of identifying the on-site users. Experiment results on a real-world Twitter dataset demonstrate our method outperforms state-of-the-art methods.