@InProceedings{rahimi-cohn-baldwin:2018:Long,
  author    = {Rahimi, Afshin  and  Cohn, Trevor  and  Baldwin, Timothy},
  title     = {Semi-supervised User Geolocation via Graph Convolutional Networks},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {2009--2019},
  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 high- way network gates are essential for control- ling the amount of useful neighbourhood expansion in GCN.},
  url       = {http://www.aclweb.org/anthology/P18-1187}
}