Discovering volatile events in your neighborhood: Local-area topic extraction from blog entries

Abstract

This paper presents a method for the detection of occasional or volatile local events using topic extraction technologies. This is a new application of topic extraction technologies that has not been addressed in general location-based services. A two-level hierarchical clustering method was applied to topics and their transitions using time-series blog entries collected with search queries including place names. According to experiments using 764 events from 37 locations in Tokyo and its vicinity, our method achieved 77.0% event findability. It was found that the number of blog entries in urban areas was sufficient for the extraction of topics, and the proposed method could extract typical volatile events, such as performances of music groups, and places of interest, such as popular restaurants. © 2009 Springer Berlin Heidelberg.