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Soc Sci Med. 2017 Oct;191:168-175. doi: 10.1016/j.socscimed.2017.08.041. Epub 2017 Sep 4.

Geographic and demographic correlates of autism-related anti-vaccine beliefs on Twitter, 2009-15.

Author information

1
Department of Psychology, The University of Alabama, Tuscaloosa, AL, United States. Electronic address: tstomeny@ua.edu.
2
College of Media, Communication and Information, University of Colorado-Boulder, Boulder, CO, United States.
3
Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, United States.

Abstract

This study examines temporal trends, geographic distribution, and demographic correlates of anti-vaccine beliefs on Twitter, 2009-2015. A total of 549,972 tweets were downloaded and coded for the presence of anti-vaccine beliefs through a machine learning algorithm. Tweets with self-disclosed geographic information were resolved and United States Census data were collected for corresponding areas at the micropolitan/metropolitan level. Trends in number of anti-vaccine tweets were examined at the national and state levels over time. A least absolute shrinkage and selection operator regression model was used to determine census variables that were correlated with anti-vaccination tweet volume. Fifty percent of our sample of 549,972 tweets collected between 2009 and 2015 contained anti-vaccine beliefs. Anti-vaccine tweet volume increased after vaccine-related news coverage. California, Connecticut, Massachusetts, New York, and Pennsylvania had anti-vaccination tweet volume that deviated from the national average. Demographic characteristics explained 67% of variance in geographic clustering of anti-vaccine tweets, which were associated with a larger population and higher concentrations of women who recently gave birth, households with high income levels, men aged 40 to 44, and men with minimal college education. Monitoring anti-vaccination beliefs on Twitter can uncover vaccine-related concerns and misconceptions, serve as an indicator of shifts in public opinion, and equip pediatricians to refute anti-vaccine arguments. Real-time interventions are needed to counter anti-vaccination beliefs online. Identifying clusters of anti-vaccination beliefs can help public health professionals disseminate targeted/tailored interventions to geographic locations and demographic sectors of the population.

KEYWORDS:

Autism spectrum disorder; Beliefs; Big data; Machine learning algorithms; Social media; Twitter; Vaccines

PMID:
28926775
PMCID:
PMC5623105
[Available on 2018-10-01]
DOI:
10.1016/j.socscimed.2017.08.041
[Indexed for MEDLINE]

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