Analysis of crowds' movement using Twitter

Abstract

Over the last decade, the infrastructure supporting the smart city has lived together with and was surpassed by the rise of social media. The tremendous growth of both mobile devices and social media users has unearthed a new kind of services in the so-called location-based social networks (LBSNs). In this new scenario, the term crowdsensing refers to sharing data collected by sensing humans with the aim of measuring phenomena of common interest. Crowd-sourced location data provide the ability to study, for the first time, the movement of individuals in urban environments. In this paper, we address the problem of monitoring crowds, whereabouts and movement, which can assist decision making in education, emergency training, urban planning, traffic engineering, etc. Precisely, two-phase density-based analysis for collectives and crowds (2PD-CC) is a novel methodology over public data in LBSN, which combines density-based clustering, outlier detection a topic modeling over a region under study to detect, predict, and explain abnormal group behavior. In order to validate the methodology and its potential application to full-scale problems, an experiment over Twitter data was performed in Madrid city.