Mapping information exposure on social media to explain differences in HPV vaccine coverage in the United States

Adam G. Dunn, Didi Surian, Julie Leask, Aditi Dey, Kenneth D. Mandl, Enrico Coiera

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Background: Together with access, acceptance of vaccines affects human papillomavirus (HPV) vaccine coverage, yet little is known about media's role. Our aim was to determine whether measures of information exposure derived from Twitter could be used to explain differences in coverage in the United States. Methods: We conducted an analysis of exposure to information about HPV vaccines on Twitter, derived from 273.8 million exposures to 258,418 tweets posted between 1 October 2013 and 30 October 2015. Tweets were classified by topic using machine learning methods. Proportional exposure to each topic was used to construct multivariable models for predicting state-level HPV vaccine coverage, and compared to multivariable models constructed using socioeconomic factors: poverty, education, and insurance. Outcome measures included correlations between coverage and the individual topics and socioeconomic factors; and differences in the predictive performance of the multivariable models. Results: Topics corresponding to media controversies were most closely correlated with coverage (both positively and negatively); education and insurance were highest among socioeconomic indicators. Measures of information exposure explained 68% of the variance in one dose 2015 HPV vaccine coverage in females (males: 63%). In comparison, models based on socioeconomic factors explained 42% of the variance in females (males: 40%). Conclusions: Measures of information exposure derived from Twitter explained differences in coverage that were not explained by socioeconomic factors. Vaccine coverage was lower in states where safety concerns, misinformation, and conspiracies made up higher proportions of exposures, suggesting that negative representations of vaccines in the media may reflect or influence vaccine acceptance.

LanguageEnglish
Pages3033-3040
Number of pages8
JournalVaccine
Volume35
Issue number23
DOIs
Publication statusPublished - 25 May 2017

Fingerprint

Social Media
Papillomavirus Vaccines
social networks
Papillomaviridae
Vaccines
vaccines
socioeconomic factors
Insurance
Education
insurance
Poverty
education
Communication
misinformation
Outcome Assessment (Health Care)
Safety
exposure assessment
artificial intelligence
poverty
socioeconomics

Bibliographical note

Copyright the Author(s) 2017. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • Acceptability
  • Content analysis
  • Human papillomavirus vaccine
  • Immunization coverage
  • Social media
  • Vaccine refusal

Cite this

@article{53907ae2bef544369ca1daeb4e01760a,
title = "Mapping information exposure on social media to explain differences in HPV vaccine coverage in the United States",
abstract = "Background: Together with access, acceptance of vaccines affects human papillomavirus (HPV) vaccine coverage, yet little is known about media's role. Our aim was to determine whether measures of information exposure derived from Twitter could be used to explain differences in coverage in the United States. Methods: We conducted an analysis of exposure to information about HPV vaccines on Twitter, derived from 273.8 million exposures to 258,418 tweets posted between 1 October 2013 and 30 October 2015. Tweets were classified by topic using machine learning methods. Proportional exposure to each topic was used to construct multivariable models for predicting state-level HPV vaccine coverage, and compared to multivariable models constructed using socioeconomic factors: poverty, education, and insurance. Outcome measures included correlations between coverage and the individual topics and socioeconomic factors; and differences in the predictive performance of the multivariable models. Results: Topics corresponding to media controversies were most closely correlated with coverage (both positively and negatively); education and insurance were highest among socioeconomic indicators. Measures of information exposure explained 68{\%} of the variance in one dose 2015 HPV vaccine coverage in females (males: 63{\%}). In comparison, models based on socioeconomic factors explained 42{\%} of the variance in females (males: 40{\%}). Conclusions: Measures of information exposure derived from Twitter explained differences in coverage that were not explained by socioeconomic factors. Vaccine coverage was lower in states where safety concerns, misinformation, and conspiracies made up higher proportions of exposures, suggesting that negative representations of vaccines in the media may reflect or influence vaccine acceptance.",
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author = "Dunn, {Adam G.} and Didi Surian and Julie Leask and Aditi Dey and Mandl, {Kenneth D.} and Enrico Coiera",
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Mapping information exposure on social media to explain differences in HPV vaccine coverage in the United States. / Dunn, Adam G.; Surian, Didi; Leask, Julie; Dey, Aditi; Mandl, Kenneth D.; Coiera, Enrico.

In: Vaccine, Vol. 35, No. 23, 25.05.2017, p. 3033-3040.

Research output: Contribution to journalArticleResearchpeer-review

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T1 - Mapping information exposure on social media to explain differences in HPV vaccine coverage in the United States

AU - Dunn, Adam G.

AU - Surian, Didi

AU - Leask, Julie

AU - Dey, Aditi

AU - Mandl, Kenneth D.

AU - Coiera, Enrico

N1 - Copyright the Author(s) 2017. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

PY - 2017/5/25

Y1 - 2017/5/25

N2 - Background: Together with access, acceptance of vaccines affects human papillomavirus (HPV) vaccine coverage, yet little is known about media's role. Our aim was to determine whether measures of information exposure derived from Twitter could be used to explain differences in coverage in the United States. Methods: We conducted an analysis of exposure to information about HPV vaccines on Twitter, derived from 273.8 million exposures to 258,418 tweets posted between 1 October 2013 and 30 October 2015. Tweets were classified by topic using machine learning methods. Proportional exposure to each topic was used to construct multivariable models for predicting state-level HPV vaccine coverage, and compared to multivariable models constructed using socioeconomic factors: poverty, education, and insurance. Outcome measures included correlations between coverage and the individual topics and socioeconomic factors; and differences in the predictive performance of the multivariable models. Results: Topics corresponding to media controversies were most closely correlated with coverage (both positively and negatively); education and insurance were highest among socioeconomic indicators. Measures of information exposure explained 68% of the variance in one dose 2015 HPV vaccine coverage in females (males: 63%). In comparison, models based on socioeconomic factors explained 42% of the variance in females (males: 40%). Conclusions: Measures of information exposure derived from Twitter explained differences in coverage that were not explained by socioeconomic factors. Vaccine coverage was lower in states where safety concerns, misinformation, and conspiracies made up higher proportions of exposures, suggesting that negative representations of vaccines in the media may reflect or influence vaccine acceptance.

AB - Background: Together with access, acceptance of vaccines affects human papillomavirus (HPV) vaccine coverage, yet little is known about media's role. Our aim was to determine whether measures of information exposure derived from Twitter could be used to explain differences in coverage in the United States. Methods: We conducted an analysis of exposure to information about HPV vaccines on Twitter, derived from 273.8 million exposures to 258,418 tweets posted between 1 October 2013 and 30 October 2015. Tweets were classified by topic using machine learning methods. Proportional exposure to each topic was used to construct multivariable models for predicting state-level HPV vaccine coverage, and compared to multivariable models constructed using socioeconomic factors: poverty, education, and insurance. Outcome measures included correlations between coverage and the individual topics and socioeconomic factors; and differences in the predictive performance of the multivariable models. Results: Topics corresponding to media controversies were most closely correlated with coverage (both positively and negatively); education and insurance were highest among socioeconomic indicators. Measures of information exposure explained 68% of the variance in one dose 2015 HPV vaccine coverage in females (males: 63%). In comparison, models based on socioeconomic factors explained 42% of the variance in females (males: 40%). Conclusions: Measures of information exposure derived from Twitter explained differences in coverage that were not explained by socioeconomic factors. Vaccine coverage was lower in states where safety concerns, misinformation, and conspiracies made up higher proportions of exposures, suggesting that negative representations of vaccines in the media may reflect or influence vaccine acceptance.

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KW - Content analysis

KW - Human papillomavirus vaccine

KW - Immunization coverage

KW - Social media

KW - Vaccine refusal

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