Extracting health-related causality from twitter messages using natural language processing-Reference-Cited by-同舟云学术

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Extracting health-related causality from twitter messages using natural language processing

Published:2019-04 Issue:S3 Volume:19 Page:
ISSN:1472-6947
Container-title:BMC Medical Informatics and Decision Making
language:en
Short-container-title:BMC Med Inform Decis Mak

Author:

Doan Son,Yang Elly W.,Tilak Sameer S.,Li Peter W.,Zisook Daniel S.,Torii Manabu

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Health Policy,Computer Science Applications

Link

http://link.springer.com/content/pdf/10.1186/s12911-019-0785-0.pdf

Reference38 articles.

1. Paul MJ, Dredze M, Broniatowski D. Twitter improves influenza forecasting. PLOS Currents Outbreaks. Edition 1. 2014;1-13.

2. Stefanidis A, Vraga E, Lamprianidis G, Radzikowski J, Delamater PL, Jacobsen KH, et al. Zika in twitter: temporal variations of locations, actors, and concepts. JMIR public Heal Surveill. 2017;3:e22.

3. Collier N, Doan S. Syndromic classification of Twitter messages. Electron Healthc. Springer Berlin Heidelberg; 2012. p. 186–95.

4. Sueki H. The association of suicide-related twitter use with suicidal behaviour: a cross-sectional study of young internet users in Japan. J Affect Disord. 2015;170:155–60.

5. Burnap P, Colombo G, Amery R, Hodorog A, Scourfield J. Multi-class machine classification of suicide-related communication on twitter. Online Soc networks media. 2017;2:32–44.

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