@InProceedings{chi-EtAl:2016:WNUT,
  author    = {Chi, Lianhua  and  Lim, Kwan Hui  and  Alam, Nebula  and  Butler, Christopher J.},
  title     = {Geolocation Prediction in Twitter Using Location Indicative Words and Textual Features},
  booktitle = {Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {227--234},
  abstract  = {Knowing the location of a social media user and their posts is important for
	various purposes, such as the recommendation of location-based items/services,
	and locality detection of crisis/disasters. This paper describes our submission
	to the shared task ``Geolocation Prediction in Twitter" of the 2nd Workshop on
	Noisy User-generated Text. In this shared task, we propose an algorithm to
	predict the location of Twitter users and tweets using a multinomial Naive
	Bayes classifier trained on Location Indicative Words and various textual
	features (such as city/country names, \#hashtags and $@$mentions). We compared our
	approach against various baselines based on Location Indicative Words,
	city/country names, \#hashtags and $@$mentions as individual feature sets, and
	experimental results show that our approach outperforms these baselines in
	terms of classification accuracy, mean and median error distance.},
  url       = {http://aclweb.org/anthology/W16-3930}
}