Using a Recurrent Neural Network Model for Classification of Tweets Conveyed Influenza-related Information

Chen-Kai Wang, Onkar Singh, Zhao-Li Tang, Hong-Jie Dai


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.
Anthology ID:
W17-5805
Volume:
Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017)
Month:
November
Year:
2017
Address:
Taipei, Taiwan
Venue:
WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
33–38
Language:
URL:
https://aclanthology.org/W17-5805
DOI:
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PDF:
https://aclanthology.org/W17-5805.pdf