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

Thiago Marques, Sidemar Cezário, Juciano Lacerda, Rafael Pinto (), Lyrene Silva, Orivaldo Santana, Anna Giselle Ribeiro, Agnaldo Souza Cruz, Angélica Espinosa Miranda, Aedê Cadaxa, Lucía Sanjuán Núñez, Hugo Gonçalo Oliveira, Rifat Atun and Ricardo Valentim
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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
Rafael Pinto: Department of Informatics and Applied Mathematics, Federal University of Rio Grande do Norte, Natal 59078-970, 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
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
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
Ricardo Valentim: Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte, Natal 59010-090, Brazil

IJERPH, 2022, vol. 19, issue 24, 1-13

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).

Keywords: sentiment analysis; public health; digital solution; online news; public policy (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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