Twitter User Location Inference Based on Representation Learning and Label Propagation

Abstract

Social network user location inference technology has been widely used in various geospatial applications like public health monitoring and local advertising recommendation. Due to insufficient consideration of relationships between users and location indicative words, most of existing inference methods estimate label propagation probabilities solely based on statistical features, resulting in large location inference error. In this paper, a Twitter user location inference method based on representation learning and label propagation is proposed. Firstly, the heterogeneous connection relation graph is constructed based on relationships between Twitter users and relationships between users and location indicative words, and relationships unrelated to geographic attributes are filtered. Then, vector representations of users are learnt from the connection relation graph. Finally, label propagation probabilities between adjacent users are calculated based on vector representations, and the locations of unknown users are predicted through iterative label propagation. Experiments on two representative Twitter datasets - GeoText and TwUs, show that the proposed method can accurately calculate label propagation probabilities based on vector representations and improve the accuracy of location inference. Compared with existing typical Twitter user location inference methods - GCN and MLP-TXT+NET, the median error distance of the proposed method is reduced by 18% and 16%, respectively.