ac58dab46da555f333539d8559c420c3 pmc_9779517.pdf a167a880db57fed70d68e112950ad25e572cb81e pmc_9779517.pdf e19b359abdc8ac2fffc07df8846df4f11b49a7aaa71a3d9dcd710e97e9682fd7 pmc_9779517.pdf Title: Sentiment Analysis in Understanding the Potential of Online News in the Public Health Crisis Response Subject: 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 = 0.33 correlation between TP3 news and testing data (p-value = 0.04); the years 2018 and 2019 presented a = 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 Author: 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 Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Tue Dec 20 11:19:15 2022 CET ModDate: Tue Dec 20 11:42:55 2022 CET Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 13 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 1986702 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- FPMJHL+VnURWPalladioL-Bold Type 1 Custom yes yes no 10 0 MQBVRE+VnURWPalladioL Type 1 Custom yes yes no 15 0 AEBBVM+EURM10 Type 1 Builtin yes yes no 19 0 LQVUNY+URWPalladioL-Bold Type 1 Custom yes yes no 23 0 NTNNLW+URWPalladioL-Roma Type 1 Custom yes yes no 28 0 SMZQGI+URWPalladioL-Ital Type 1 Custom yes yes no 32 0 LWQXII+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 50 0 IRWHZX+TimesNewRomanPSMT Type 1C WinAnsi yes yes no 53 0 CFKCKA+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 87 0 CFKCKC+PalatinoLinotype TrueType WinAnsi yes yes no 90 0 CFKCKE+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 93 0 TRSMUE+CMSY10 Type 1 Builtin yes yes no 100 0 IOLAWT+PazoMath-Italic Type 1 Builtin yes yes no 104 0 FERHKG+CMR10 Type 1 Builtin yes yes no 108 0 CFKCKA+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 121 0 CFKCKC+PalatinoLinotype TrueType WinAnsi yes yes no 124 0 CFKCKE+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 127 0 CFKCKA+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 135 0 CFKCKC+PalatinoLinotype TrueType WinAnsi yes yes no 138 0 CFKCKE+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 141 0 CFKCKA+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 149 0 CFKCKB+PalatinoLinotype CID TrueType Identity-H yes yes yes 152 0 CFKCKC+PalatinoLinotype TrueType WinAnsi yes yes no 158 0 CFKCKE+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 161 0 CFKCKA+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 172 0 CFKCKB+PalatinoLinotype CID TrueType Identity-H yes yes yes 175 0 CFKCKC+PalatinoLinotype TrueType WinAnsi yes yes no 181 0 CFKCKE+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 184 0 AEDGTQ+VnURWPalladioL-Italic Type 1 Custom yes yes no 199 0 Jhove (Rel. 1.22.1, 2019-04-17) Date: 2023-09-28 02:34:01 CEST RepresentationInformation: pmc_9779517.pdf ReportingModule: PDF-hul, Rel. 1.12.1 (2019-04-17) LastModified: 2023-09-27 02:17:11 CEST Size: 1986702 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 347 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@262b2c86 Children: Item: Title: Related Work Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@371a67ec Item: Title: Study Design Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@5ed828d Item: Title: Materials and Methods Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@50d0686 Children: Item: Title: Use of Data Sources Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@7a3d45bd Item: Title: VADER Algorithm Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1e7c7811 Item: Title: Statistical Methods Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@77ec78b9 Item: Title: Results Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1a3869f4 Item: Title: Discussion Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@a38d7a3 Item: Title: Conclusions Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@77f99a05 Item: Title: References Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@63440df3 Info: Title: Sentiment Analysis in Understanding the Potential of Online News in the Public Health Crisis Response Author: 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 Subject: 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 = 0.33 correlation between TP3 news and testing data (p-value = 0.04); the years 2018 and 2019 presented a = 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). 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