Understanding Event Organization at Scale in Event-Based Social Networks

Abstract

Understanding real-world event participation behavior has been a subject of active research and can offer valuable insights for event-related recommendation and advertisement. The emergence of event-based social networks (EBSNs), which attracts online users to host/attend offline events, has enabled exciting new research in this domain. However, most existing works focus on understanding or predicting individual users’ event participation behavior or recommending events to individual users. Few studies have addressed the problem of event popularity from the event organizer’s point of view. In this work, we study the latent factors for determining event popularity using large-scale datasets collected from the popular Meetup.com EBSN in five major cities around the world. We analyze and model four contextual factors: spatial factor using location convenience, quality, popularity density, and competitiveness; group factor using group member entropy and loyalty; temporal factor using temporal preference and weekly event patterns; and semantic factor using readability, sentiment, part of speech, and text novelty. In addition, we have 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 COSINE can capture the diverse influential factors of event participation and can be used by event organizers to predict/improve the popularity of their events. Detailed evaluations demonstrate that our COSINE framework achieves high accuracy for event popularity prediction in all five cities with diverse cultures and user event behaviors.