@InProceedings{wang-EtAl:2017:DDDSM,
  author    = {Wang, Chen-Kai  and  Singh, Onkar  and  Tang, Zhao-Li  and  Dai, Hong-Jie},
  title     = {Using a Recurrent Neural Network Model for Classification of Tweets Conveyed Influenza-related Information},
  booktitle = {Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Association for Computational Linguistics},
  pages     = {33--38},
  abstract  = {Traditional disease surveillance systems depend on outpatient reporting and
	virological test results released by hospitals. These data have valid and
	accurate information about emerging outbreaks but it’s often not timely. In
	recent years the exponential growth of users getting connected to social media
	provides immense knowledge about epidemics by sharing related information.
	Social media can now flag more immediate concerns related to out-breaks in real
	time. In this paper we apply the long short-term memory recurrent neural
	net-work (RNN) architecture to classify tweets conveyed influenza-related
	information and compare its performance with baseline algorithms including
	support vector machine (SVM), decision tree, naive Bayes, simple logistics, and
	naive Bayes multinomial. The developed RNN model achieved an F-score of 0.845
	on the MedWeb task test set, which outperforms the F-score of SVM without
	applying the synthetic minority oversampling technique by 0.08. The F-score of
	the RNN model is within 1% of the highest score achieved by SVM with
	oversampling technique.},
  url       = {http://www.aclweb.org/anthology/W17-5805}
}