Mapping information exposure on social media to explain differences in HPV vaccine coverage in the United States. Issue 23 (25th May 2017)

• Record Type: Journal Article
• Authors: Dunn, Adam G.; Surian, Didi; Leask, Julie; Dey, Aditi; Mandl, Kenneth D.; Coiera, Enrico
• Is Part Of: Vaccine. Volume 35:Issue 23(2017)
• Page Start: 3033
• Page End: 3040
• Publication Date: 2017-05-25
• Subjects: HPV human papillomavirus; Human papillomavirus vaccine -- Social media -- Content analysis -- Acceptability -- Vaccine refusal -- Immunization coverage; Vaccines -- Periodicals; 615.372
• DOI: 10.1016/j.vaccine.2017.04.060
• Languages: English
• ISSNs: 0264-410X

Abstract

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.