On infectious intestinal disease surveillance using social media content
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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On infectious intestinal disease surveillance using social media content. / Zou, Bin; Lampos, Vasileios; Gorton, Russell; Cox, Ingemar Johansson.
DH '16: Proceedings of the 2016 Digital Health Conference. Association for Computing Machinery, 2016. s. 157-161.Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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TY - GEN
T1 - On infectious intestinal disease surveillance using social media content
AU - Zou, Bin
AU - Lampos, Vasileios
AU - Gorton, Russell
AU - Cox, Ingemar Johansson
N1 - Conference code: 6
PY - 2016
Y1 - 2016
N2 - This paper investigates whether infectious intestinal diseases (IIDs) can be detected and quantified using social media content. Experiments are conducted on user-generated data from the microblogging service, Twitter. Evaluation is based on the comparison with the number of IID cases reported by traditional health surveillance methods. We employ a deep learning approach for creating a topical vocabulary, and then apply a regularised linear (Elastic Net) as well as a nonlinear (Gaussian Process) regression function for inference. We show that like previous text regression tasks, the nonlinear approach performs better. In general, our experimental results, both in terms of predictive performance and semantic interpretation, indicate that Twitter data contain a signal that could be strong enough to complement conventional methods for IID surveillance.
AB - This paper investigates whether infectious intestinal diseases (IIDs) can be detected and quantified using social media content. Experiments are conducted on user-generated data from the microblogging service, Twitter. Evaluation is based on the comparison with the number of IID cases reported by traditional health surveillance methods. We employ a deep learning approach for creating a topical vocabulary, and then apply a regularised linear (Elastic Net) as well as a nonlinear (Gaussian Process) regression function for inference. We show that like previous text regression tasks, the nonlinear approach performs better. In general, our experimental results, both in terms of predictive performance and semantic interpretation, indicate that Twitter data contain a signal that could be strong enough to complement conventional methods for IID surveillance.
KW - Disease surveillance
KW - IID
KW - Infectious intestinal disease
KW - Social media
KW - Twitter
KW - User-generated content
KW - Word embeddings
U2 - 10.1145/2896338.2896372
DO - 10.1145/2896338.2896372
M3 - Article in proceedings
AN - SCOPUS:84966605239
SP - 157
EP - 161
BT - DH '16
PB - Association for Computing Machinery
T2 - 6th International Conference on Digital Health
Y2 - 11 April 2016 through 13 April 2016
ER -
ID: 168288069