In this paper, we address the challenge of modeling the size, duration, and temporal dynamics of short-lived crowds that manifest in social media. Successful population modeling for crowds is critical for many services including location recommendation, traffic prediction, and advertising. However, crowd modeling is challenging since 1) user-contributed data in social media is noisy and oftentimes incomplete, in the sense that users only reveal when they join a crowd through posts but not when they depart; and 2) the size of short-lived crowds typically changes rapidly, growing and shrinking in sharp bursts. Toward robust population modeling, we first propose a duration model to predict the time users spend in a particular crowd. We propose a time-evolving population model for estimating the number of people departing a crowd, which enables the prediction of the total population remaining in a crowd. Based on these population models, we further describe an approach that allows us to predict the number of posts generated from a crowd. We validate the crowd models through extensive experiments over 22 million geo-location based check-ins and 120,000 event-related tweets. Copyright 2013 ACM.
name of conference
Proceedings of the 24th ACM Conference on Hypertext and Social Media
