UGCC: Social Media User Geolocation via Cyclic Coupling

Abstract

Social media user geolocation is to infer users’ resident locations based on social media data, including user texts and social relationships. Existing methods mainly rely on the textual feature propagation in the social graph to fuse users’ textual and social information. The geolocation accuracy is susceptible to insufficient data sources and inadequate fusion. In this paper, a social media user geolocation algorithm based on cyclic coupling (called UGCC) is proposed. We collapse the social graph based on the neighbor location proximity, which reduces noisy information while enriching social relationships. Unlike existing methods that ignore the social graph's structure, UGCC measures the probability of users being in the candidate locations according to users’ structural location in the social sub-graph. Finally, we design a cyclic coupling mechanism to fuse the users’ textual and social information, which enables the two kinds of information to enhance each other and geolocate users cooperatively. Compared with ten typical existing methods (such as RELP and HGNN), experimental results show UGCC's superior performance. On two public datasets, the city-level accuracies of UGCC reach 40.8% and 50.1%; the median errors are 35.1% and 23.4% lower than the state-of-the-art methods.