ClarTM: a method for geolocations clarification within extensive urban sites using topic modelling

Abstract

Determining the correct geolocation of user posts in social networks plays a vital role in many studies investigating and improving the urban environment. We started our research on the possibilities of refining geolocations in extended urban objects, where posts refer to locations exceptionally unevenly, and we continue it in this work, supplementing the location redefinition algorithms with the new methods. Namely, we have developed a three-stage location redefinition algorithm. In the first stage, it expands geolocations from external sources, enriches them with descriptions and combines geolocations from different sources. The second stage redefines posts to locations based on recognising named entities and semantic similarity metrics. And in the third stage, it eliminates inaccuracies in popular geolocations using topic modelling and improves the quality of geolocation clarification by redefining posts to more appropriate geolocations. Topic modelling, which found topics to redefine in 7 (11% of all) locations, increased the redefinition's accuracy by 30% (from 57% to 87%).