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Identification of Keywords From Twitter and Web Blog Posts to Detect Influenza Epidemics in Korea

Published online by Cambridge University Press:  31 July 2017

Hyekyung Woo ,
Hyeon Sung Cho ,
Eunyoung Shim ,
Jong Koo Lee ,
Kihwang Lee ,
Gilyoung Song  and
Youngtae Cho
Hyekyung Woo
Affiliation:
Department of Public Health Science, School of Public Health, Seoul National University, Seoul, Korea
Hyeon Sung Cho
Affiliation:
Department of Intelligent Cognitive Technology Research, Electronics and Telecommunications Research Institute, Daejeon, Korea
Eunyoung Shim
Affiliation:
Department of Public Health Science, School of Public Health, Seoul National University, Seoul, Korea Department of New Business, Samsung Fire and Marine Insurance, Seoul, Korea
Jong Koo Lee
Affiliation:
College of Medicine, Seoul National University, Seoul, Korea
Kihwang Lee
Affiliation:
Mining Laboratory, Daumsoft, Seoul, Korea
Gilyoung Song
Affiliation:
Mining Laboratory, Daumsoft, Seoul, Korea
Youngtae Cho*
Affiliation:
Department of Public Health Science, School of Public Health, Seoul National University, Seoul, Korea
*
Correspondence and reprint requests to Youngtae Cho, Department of Public Health Science, School of Public Health, Seoul National University, 1 Kwanak-ro, Kwanak-gu, Seoul 151-742, Korea (e-mail: [email protected]).
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Abstract

Objective

Social media data are a highly contextual health information source. The objective of this study was to identify Korean keywords for detecting influenza epidemics from social media data.

Methods

We included data from Twitter and online blog posts to obtain a sufficient number of candidate indicators and to represent a larger proportion of the Korean population. We performed the following steps: initial keyword selection; generation of a keyword time series using a preprocessing approach; optimal feature selection; model building and validation using least absolute shrinkage and selection operator, support vector machine (SVM), and random forest regression (RFR).

Results

A total of 15 keywords optimally detected the influenza epidemic, evenly distributed across Twitter and blog data sources. Model estimates generated using our SVM model were highly correlated with recent influenza incidence data.

Conclusions

The basic principles underpinning our approach could be applied to other countries, languages, infectious diseases, and social media sources. Social media monitoring using our approach may support and extend the capacity of traditional surveillance systems for detecting emerging influenza. (Disaster Med Public Health Preparedness. 2018; 12: 352–359)

Keywords

influenzaepidemicssurveillancesocial mediaKorea
Type
Original Research
Information
Disaster Medicine and Public Health Preparedness , Volume 12 , Issue 3 , June 2018 , pp. 352 - 359
Copyright
Copyright © Society for Disaster Medicine and Public Health, Inc. 2017 

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