SemAGR: semantic method for accurate geolocations reconstruction within extensive urban sites

Abstract

Accurate geolocation of users’ posts in social networks plays a vital role in a wide area of research devoted to analysing the urban environment based on social media data. It allows us to effectively correlate information about a real urban object with its description in social networks. Information analysis about extensive urban sites is challenging in such studies because publications correlate extremely unevenly with marked geotags relating to such objects. In addition, users of social networks often intentionally indicate incorrect geolocations to increase their publications’ popularity. This article proposes a solution to the problem of accurate geolocation reconstruction for extensive urban sites. It is proposed to use a combined method that enriches the list of initial geolocations of the social network with the help of external sources. The transformer model is then applied to recognise named entities that help to detect mentions of events or locations in user posts. After detection, posts are redefined to the new locations from the extended list according to the semantic similarity calculated between publications and locations.