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

Abstract

AbstractObjectiveSocial 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.MethodsWe 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).ResultsA 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.ConclusionsThe 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)