Semi-supervised User Geolocation via Graph Convolutional Networks

2018/04/22 by Rahimi, Afshin, Cohn, Trevor, Baldwin, Timothy · 1 citation
#Computation and Language (cs.CL) #FOS: Computer and information sciences

paper · doi:10.48550/arxiv.1804.08049

Abstract

Social media user geolocation is vital to many applications such as event detection. In this paper, we propose GCN, a multiview geolocation model based on Graph Convolutional Networks, that uses both text and network context. We compare GCN to the state-of-the-art, and to two baselines we propose, and show that our model achieves or is competitive with the state- of-the-art over three benchmark geolocation datasets when sufficient supervision is available. We also evaluate GCN under a minimal supervision scenario, and show it outperforms baselines. We find that highway network gates are essential for controlling the amount of useful neighbourhood expansion in GCN.

Cited by

• Forecasting West Nile virus with deep graph encoders

Related

• Exploiting Text and Network Context for Geolocation of Social Media\n Users
• Twitter User Geolocation using Deep Multiview Learning
• Designing weighted and multiplex networks for deep learning user geolocation in Twitter
• Twitter User Geolocation Using a Unified Text and Network Prediction\n Model
• A Probabilistic Framework for Location Inference from Social Media