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Sentiment Analysis in Understanding the Potential of Online News in the Public Health Crisis Response

Author

Listed:
  • Thiago Marques

    (Department of Informatics and Applied Mathematics, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil)

  • Sidemar Cezário

    (Department of Informatics and Applied Mathematics, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil)

  • Juciano Lacerda

    (Department of Social Communication, Federal University of Rio Grande do Norte, Natal 59072-970, Brazil
    Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte, Natal 59010-090, Brazil)

  • Rafael Pinto

    (Department of Informatics and Applied Mathematics, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil
    Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte, Natal 59010-090, Brazil
    Information Systems Coordination, Federal Institute of Rio Grande do Norte, Natal 59015-300, Brazil)

  • Lyrene Silva

    (Department of Informatics and Applied Mathematics, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil)

  • Orivaldo Santana

    (Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte, Natal 59010-090, Brazil
    School of Science and Technology, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil)

  • Anna Giselle Ribeiro

    (Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte, Natal 59010-090, Brazil)

  • Agnaldo Souza Cruz

    (Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte, Natal 59010-090, Brazil)

  • Angélica Espinosa Miranda

    (Ministry of Health, Brasília 70070-600, Brazil
    Postgraduate Program in Infectious Diseases, Federal University of Espírito Santo, Vitória 29075-910, Brazil)

  • Aedê Cadaxa

    (Ministry of Health, Brasília 70070-600, Brazil)

  • Lucía Sanjuán Núñez

    (Department of Social and Cultural Anthropology, Autonomous University of Barcelona, 08193 Barcelona, Spain)

  • Hugo Gonçalo Oliveira

    (Centre for Informatics and Systems of the University of Coimbra (CISUC), Department of Informatics Engineering (DEI), University of Coimbra, 3030-290 Coimbra, Portugal)

  • Rifat Atun

    (Health Systems Innovation Laboratory, Harvard TH Chan School Public Health, Harvard University, Boston, MA 02115, USA
    Department of Global Health and Population, Harvard TH Chan School Public Health, Harvard University, Boston, MA 02115, USA)

  • Ricardo Valentim

    (Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte, Natal 59010-090, Brazil
    Department of Biomedical Engineering, Federal University of Rio Grande do Norte, Natal 59628-330, Brazil)

Abstract

This study analyzes online news disseminated throughout the pre-, during-, and post-intervention periods of the “Syphilis No!” Project, which was developed in Brazil between November 2018 and March 2019. We investigated the influence of sentiment aspects of news to explore their possible relationships with syphilis testing data in response to the syphilis epidemic in Brazil. A dictionary-based technique (VADER) was chosen to perform sentiment analysis considering the Brazilian Portuguese language. Finally, the data collected were used in statistical tests to obtain other indicators, such as correlation and distribution analysis. Of the 627 news items, 198 (31.58%) were classified as a sentiment of security (TP2; stands for the news type 2), whereas 429 (68.42%) were classified as sentiments that instilled vulnerability (TP3; stands for the news type 3). The correlation between the number of syphilis tests and the number of news types TP2 and TP3 was verified from (i) 2015 to 2017 and (ii) 2018 to 2019. For the TP2 type news, in all periods, the p -values were greater than 0.05, thus generating inconclusive results. From 2015 to 2017, there was an ρ = 0.33 correlation between TP3 news and testing data ( p -value = 0.04); the years 2018 and 2019 presented a ρ = 0.67 correlation between TP3 news and the number of syphilis tests performed per month, with p -value = 0.0003. In addition, Granger’s test was performed between TP3 news and syphilis testing, which resulted in a p -value = 0.002, thus indicating the existence of Granger causality between these time series. By applying natural language processing to sentiment and informational content analysis of public health campaigns, it was found that the most substantial increase in testing was strongly related to attitude-inducing content (TP3).

Suggested Citation

  • Thiago Marques & Sidemar Cezário & Juciano Lacerda & Rafael Pinto & Lyrene Silva & Orivaldo Santana & Anna Giselle Ribeiro & Agnaldo Souza Cruz & Angélica Espinosa Miranda & Aedê Cadaxa & Lucía Sanjuá, 2022. "Sentiment Analysis in Understanding the Potential of Online News in the Public Health Crisis Response," IJERPH, MDPI, vol. 19(24), pages 1-13, December.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:24:p:16801-:d:1003314
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    References listed on IDEAS

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    1. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    2. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    3. Shapiro, Adam Hale & Sudhof, Moritz & Wilson, Daniel J., 2022. "Measuring news sentiment," Journal of Econometrics, Elsevier, vol. 228(2), pages 221-243.
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