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Prediction of infectious diseases using sentiment analysis on social media data

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  • Youngchul Song
  • Byungun Yoon

Abstract

As the influence and risk of infectious diseases increase, efforts are being made to predict the number of confirmed infectious disease patients, but research involving the qualitative opinions of social media users is scarce. However, social data can change the psychology and behaviors of crowds through information dissemination, which can affect the spread of infectious diseases. Existing studies have used the number of confirmed cases and spatial data to predict the number of confirmed cases of infectious diseases. However, studies using opinions from social data that affect changes in human behavior in relation to the spread of infectious diseases are inadequate. Therefore, herein, we propose a new approach for sentiment analysis of social data by using opinion mining and to predict the number of confirmed cases of infectious diseases by using machine learning techniques. To build a sentiment dictionary specialized for predicting infectious diseases, we used Word2Vec to expand the existing sentiment dictionary and calculate the daily sentiment polarity by dividing it into positive and negative polarities from collected social data. Thereafter, we developed an algorithm to predict the number of confirmed infectious patients by using both positive and negative polarities with DNN, LSTM and GRU. The method proposed herein showed that the prediction results of the number of confirmed cases obtained using opinion mining were 1.12% and 3% better than those obtained without using opinion mining in LSTM and GRU model, and it is expected that social data will be used from a qualitative perspective for predicting the number of confirmed cases of infectious diseases.

Suggested Citation

  • Youngchul Song & Byungun Yoon, 2024. "Prediction of infectious diseases using sentiment analysis on social media data," PLOS ONE, Public Library of Science, vol. 19(9), pages 1-21, September.
    • Handle: RePEc:plo:pone00:0309842
    DOI: 10.1371/journal.pone.0309842
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    References listed on IDEAS

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    1. Emrah Gecili & Assem Ziady & Rhonda D Szczesniak, 2021. "Forecasting COVID-19 confirmed cases, deaths and recoveries: Revisiting established time series modeling through novel applications for the USA and Italy," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-11, January.
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