Sentiment Analysis in Understanding the Potential of Online News in the Public Health Crisis Response
Marques, Thiago (Federal University of Rio Grande do Norte. Department of Informatics and Applied Mathematics)
Cezário, Sidemar (Federal University of Rio Grande do Norte. Department of Informatics and Applied Mathematics)
Lacerda, Juciano (Federal University of Rio Grande do Norte. Department of Social Communication)
Pinto, Rafael (Federal University of Rio Grande do Norte. Department of Informatics and Applied Mathematics)
Silva, Lyrene (Federal University of Rio Grande do Norte. Department of Informatics and Applied Mathematics)
Santana, Orivaldo (Federal University of Rio Grande do Norte. Laboratory of Technological Innovation in Health (LAIS))
Ribeiro, Anna Giselle (Federal University of Rio Grande do Norte. Laboratory of Technological Innovation in Health (LAIS))
Souza Cruz, Agnaldo (Federal University of Rio Grande do Norte. Laboratory of Technological Innovation in Health (LAIS))
Miranda, Angélica Espinosa (Brasília. Ministry of Health)
Cadaxa, Aedê (Brasília. Ministry of Health)
Sanjuán Núñez, Lucía (Universitat Autònoma de Barcelona. Departament d'Antropologia Social i Cultural)
Gonçalo Oliveira, Hugo (University of Coimbra. Department of Informatics Engineering (DEI))
Atun, Rifat (Harvard University. Department of Global Health and Population)
Valentim, Ricardo (Federal University of Rio Grande do Norte. Laboratory of Technological Innovation in Health (LAIS))

Fecha:	2022
Resumen:	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).
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Sentiment analysis ; Public health ; Digital solution ; Online news ; Public policy
Publicado en:	International journal of environmental research and public health, Vol. 19 (december 2022) , article number 16801, ISSN 1660-4601

DOI: 10.3390/ijerph192416801
PMID: 36554680

13 p, 1.9 MB

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