Identifying user geolocation with Hierarchical Graph Neural Networks and explainable fusion

Abstract

Determining user geolocation from social media data is essential in various location-based applications — from improved transportation/supply management, through providing personalized services and targeted marketing, to better overall user experiences. Previous methods rely on the similarity of user posting content and neighboring nodes for user geolocation, which suffer the problems of: (1) position-agnostic of network representation learning, which impedes the performance of their prediction accuracy; and (2) noisy and unstable user relation fusion due to the flat graph embedding methods employed. This work presents Hierarchical Graph Neural Networks (HGNN) – a novel methodology for location-aware collaborative user-aspect data fusion and location prediction. It incorporates geographical location information of users and clustering effect of regions and can capture topological relations while preserving their relative positions. By encoding the structure and features of regions with hierarchical graph learning, HGNN can primarily alleviate the problem of noisy and unstable signal fusion. We further design a relation mechanism to bridge connections between individual users and clusters, which not only leverages the information of isolated nodes that are useless in previous methods but also captures the relations between unlabeled nodes and labeled subgraphs. Furthermore, we introduce a robust statistics method to interpret the behavior of our model by identifying the importance of data samples when predicting the locations of the users. It provides meaningful explanations on the model behaviors and outputs, overcoming the drawbacks of previous approaches that treat user geolocation as “black-box” modeling and lacking interpretability. Comprehensive evaluations on real-world Twitter datasets verify the proposed model’s superior performance and its ability to interpret the user geolocation results.