Volatile Classification of Point of Interests based on Social Activity Streams

Location sharing services(LSS) like Foursquare, Gowalla and Facebook Places gather information from millions of users who leave trails in locations (i.e. chekins) in the form of micro-posts. These footprints provide a unique opportunity to explore the way in which users engage and perceive a point of interest (POI). A POI is as a human construct which describes information about locations (e.g restaurants, cities). In this work we investigate whether the collective perception of a POI can be used as a real-time dataset from which POI’s transient features can be extracted. We introduce a graph-based model for profiling geographical areas based on social awareness streams. Based on this model we define a set of measures that can characterise a location-based social awareness stream as well as act as indicators of volatile events occurring at a POI. We applied the model and measures on a dataset consisting of a collection of tweets generated at the city of Sheffield and registered over three week-ends. The model and measures introduced in this paper are relevant for design of future location-based services, real-time emergency-response models, as well as traffic forecasting. Our empirical findings demonstrate that social awareness streams not only can act as an event-sensor but also can enrich the profile of a location-entity.