Multiple-Aspect Attentional Graph Neural Networks for Online Social Network User Localization

Identifying the geographical locations of online social media users, a.k.a. user geolocation (UG), is an essential task for many location-based applications such as advertising, social event detection, emergency localization, etc. Due to the unwillingness of revealing privacy information for most users, it is challenging to directly locate users with the ground-truth geotags. Recent efforts sidestep this limitation through retrieving users & x2019; locations by alternatively unifying user generated contents (e.g., texts and public profiles) and online social relations. Though achieving some progress, previous methods rely on the similarity of texts and/or neighboring nodes for user geolocation, which suffers the problems of: (1) location-agnostic problem of network representation learning, which largely impedes the performance of their prediction accuracy; and (2) lack of interpretability w.r.t. the predicted results that is crucial for understanding model behavior and further improving prediction performance. To cope with such issues, we proposed a Multiple-aspect Attentional Graph Neural Networks (MAGNN) & x2013; a novel GNN model unifying the textual contents and interaction network for user geolocation prediction. The attention mechanism of MAGNN has the ability to capture multi-aspect information from multiple sources of data, which makes MAGNN inductive and easily adapt to few label scenarios. In addition, our model is able to provide meaningful explanations on the UG results, which is crucial for practical applications and subsequent decision makings. We conduct comprehensive evaluations over three real-world Twitter datasets. The experimental results verify the effectiveness of the proposed model compared to existing methods and shed lights on the interpretable user geolocation.