Social Media User Geolocation via Hybrid Attention

Determining user geolocation is vital to various real-world applications on the internet, such as online marketing and event detection. To identify the geolocations of users, their behaviors on social media like published posts and social interactions can be strong evidence. However, most of the existing social media based approaches individually learn from text contexts and social networks. This separation can not only lead to sub-optimal performance but also ignore the distinct importance of two resources for different users. To address this challenge, we propose a novel end-to-end framework, Hybrid-attentive User Geolocation (HUG), to jointly model post texts and user interactions in social media. The hybrid attention mechanism is introduced to automatically determine the importance of texts and social networks for each user while social media posts and interactions are modeled by a graph attention network and a language attention network. Extensive experiments conducted on three benchmark geolocation datasets using Twitter data demonstrate that HUG significantly outperforms competitive baseline methods. The in-depth analysis also indicates the robustness and interpretability of HUG.