  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.
