Semi-supervised User Geolocation via Graph Convolutional Networks

Afshin Rahimi, Trevor Cohn, Timothy Baldwin


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.
Anthology ID:
P18-1187
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2009–2019
Language:
URL:
https://www.aclweb.org/anthology/P18-1187
DOI:
10.18653/v1/P18-1187
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/P18-1187.pdf
Note:
 P18-1187.Notes.pdf
Video:
 https://vimeo.com/285805016
Presentation:
 P18-1187.Presentation.pdf