文章基本信息

标题：Uncovering Latent Mobility Patterns from Twitter During Mass Events
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作者：Enrico Steiger ; Timothy Ellersiek ; Bernd Resch 等
期刊名称：GI_FORUM - Journal for Geographic Information Science
电子版ISSN：2308-1708
出版年度：2015
页码：525-534
DOI：10.1553/giscience2015s525
出版社：ÖAW Verlag, Wien
摘要：The investigation of human activity in location-based social networks such as Twitter is onepromising example of exploring spatial structures in order to infer underlying mobilitypatterns. Previous work regarding Twitter analysis is mainly focused on the spatiotemporalclassification of events. However, since the information about the occurrence of a generalevent can in many cases be considered as given, one identified research gap is the explorationof human spatial behavior within specific mass events to potentially characterize underlying,locally occurring mobility clusters. One key challenge is to explore whether thisnoisy biased dataset can be a reliable source for the knowledge discovery of human mobilityduring mass events. In this paper we therefore present an advanced methodologycalframework, including a generative semantic topic modeling and local spatial autocorrelationapproach, to observe both spatiotemporal and semantic clusters during a major sportsevent in Boston in the US. Our results of the observed spatiotemporally and semanticallyclustered tweets within the selected case study area have shown the possibility of derivingintra-urban event related mobility patterns with similar spatiotemporal movement.