期刊名称：Bulletin of the Technical Committee on Data Engineering
出版年度：2015
卷号：38
期号：2
出版社：IEEE Computer Society
摘要：How can we automatically identify the topics of microblog posts? This question has received substantialattention in the research community and has led to the development of different topic models, whichare mathematically well-founded statistical models that enable the discovery of topics in document col-lections. Such models can be used for topic analyses according to the interests of user groups, time,geographical locations, or social behavior patterns. The increasing availability of microblog posts withassociated users, textual content, timestamps, geo-locations, and user behaviors, offers an opportunityto study space-time dependent behavioral topics. Such a topic is described by a set of words, the dis-tribution of which varies according to the time, geo-location, and behaviors (that capture how a userinteracts with other users by using functionality such as reply or re-tweet) of users. This study jointlymodels user topic interest and behaviors considering both space and time at a fine granularity. We focuson the modeling of microblog posts like Twitter tweets, where the textual content is short, but whereassociated information in the form of timestamps, geo-locations, and user interactions is available. Themodel aims to have applications in location inference, link prediction, online social profiling, etc. Wereport on experiments with tweets that offer insight into the design properties of the papers proposal.
