Events Describe Places: Tagging Places with Event Based Social Network Data

Abstract

Location based services and Geospatial web applications have become popular in recent years due to wide adoption of mobile devices. Search and recommendation of places or Points of Interests (PoIs) are prominent services available on them. The effectiveness of these services crucially depends on the availability of tags that are descriptive of places. The major geospatial databases that contain data about places suffer from the lack of descriptive tags for places, since writing them is a time-consuming process and only a few users do it despite having knowledge about places. In order to tackle this issue and automatically generate descriptive tags for places, we propose a solution that utilizes data about a set of events that happen in a specific place and use it to extract meaningful descriptive tags for that place. We use data about events held at places on Meetup, a well known event based social network and apply Latent Dirichlet Allocation (LDA) to derive sets of probable descriptive tags for any place. In order to evaluate our approach, we measure semantic relatedness between tags derived for places on Meetup and manually assigned tags from Foursquare, a location based service. Results show that event data can be used to derive semantically relevant place tags. This shows that location based services can benefit from capturing data about events to derive place tags.