Future Signals of Health and Welfare Policies and Issues using Social Big Data

Abstract

Objectives The purpose of this study is to collect health and welfare-related documents mentioned in and collectable from online channels, analyze important health and welfare keywords through topic and sentimental analyses, detect future signals concerning major policies and issues related to health and welfare services, and propose a prediction model. Methods 201,849 Health & Welfare related online documents from January 1 to March 31, 2016 from 171 Korean online channels and analyzed such documents using machine learning with random forest and Apriori algorithm association analysis. We used R software (version 3.2.1) for the association analysis data mining and visualization. Results As for the prediction of future signals of health and welfare policies, policies that were important and supported by the people were welfare payment, health promotion, job, marriage/child-birth, health insurance, and healthcare industry (in this order). Specifically, as support for documents mentioning welfare payment and jobs was high, job creation through building a spontaneous welfare system is thought to be needed. Additionally, similar to the linkage analysis result of policies, as people were against documents that mentioned only {basic pension} policies, but supported documents that included {basic pension, welfare payment, job}, there is a strong demand for the establishment of a welfare system through active self-support and labor of the elderly. Conclusions Social big data can be utilized in various areas. First, similar to the application in this study, future signals concerning government’s policies and new technologies can be predicted in advance and prepared for. Second, they can be used as a new data collection methods that supplement limitations in survey data collection systems. Finally, a preemptive response system against risk can be established through monitoring and predicting social crisis. Key words: Social big data, Machine learning, Future signals, Health & welfare

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