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Cited by 530Vaccine Coverage and HesitancyMisinformation and Its ImpactsSentiment Analysis and Opinion Mining

The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.

Assessing Vaccination Sentiments with Online Social Media: Implications for Infectious Disease Dynamics and Control

Open access
Salathé, Marcel, Khandelwal, Shashank·2011·PLoS Computational Biology
DOI 10.1371/journal.pcbi.1002199PMC PMC3192813
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Abstract

There is great interest in the dynamics of health behaviors in social networks and how they affect collective public health outcomes, but measuring population health behaviors over time and space requires substantial resources. Here, we use publicly available data from 101,853 users of online social media collected over a time period of almost six months to measure the spatio-temporal sentiment towards a new vaccine. We validated our approach by identifying a strong correlation between sentiments expressed online and CDC-estimated vaccination rates by region. Analysis of the network of opinionated users showed that information flows more often between users who share the same sentiments - and less often between users who do not share the same sentiments - than expected by chance alone. We also found that most communities are dominated by either positive or negative sentiments towards the novel vaccine. Simulations of infectious disease transmission show that if clusters of negative vaccine sentiments lead to clusters of unprotected individuals, the likelihood of disease outbreaks is greatly increased. Online social media provide unprecedented access to data allowing for inexpensive and efficient tools to identify target areas for intervention efforts and to evaluate their effectiveness.

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Crossref · 10.1371/journal.pcbi.1002199

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Figures

Figure 1 (A) Total number of negative (red), positive (green), and neutral (blue) tweets relating to influenza A(H1N1) vaccination during the Fall wave of the 2009 pandemic. (B) Daily (gray) and 14 day moving average (blue) sentiment score during the same time. (C) Correlation between estimated vaccination rates for individuals older than 6 months, and sentiment score per HHS region (black dots) and states (gray dots). Numbers represent the ten regions as defined by the US Department of Human Health & Services. Lines shows best fit of linear regression (blue for regions, red for states).
Figure 1 (A) Total number of negative (red), positive (green), and neutral (blue) tweets relating to influenza A(H1N1) vaccination during the Fall wave of the 2009 pandemic. (B) Daily (gray) and 14 day moving average (blue) sentiment score during the same time. (C) Correlation between estimated vaccination rates for individuals older than 6 months, and sentiment score per HHS region (black dots) and states (gray dots). Numbers represent the ten regions as defined by the US Department of Human Health & Services. Lines shows best fit of linear regression (blue for regions, red for states).CC BY · PubMed Central
Figure 2 (A) Proportion of negative sentiments p(-) in the network communities. Dashed line shows overall proportion in the opinionated network. The proportions of negative and positive sentiments are significantly different from the overall proportions in the entire opinionated network (with the exception of community E). (B) Effect of positive assortativity index (r) on relative risk increase (compared to risk at r∼0) of disease outbreaks that infect at least 3% of the population. Blue line shows best fit of linear regression (confidence interval based on standard error). (C) Relative risk increase (compared to risk at r∼0) of disease outbreaks of a given fraction of the population (on horizontal axis) for two values of assortativity index (r), 0.075 (red) and 0.145 (green). Note that the latter corresponds to r found in the opinionated network (see main text).
Figure 2 (A) Proportion of negative sentiments p(-) in the network communities. Dashed line shows overall proportion in the opinionated network. The proportions of negative and positive sentiments are significantly different from the overall proportions in the entire opinionated network (with the exception of community E). (B) Effect of positive assortativity index (r) on relative risk increase (compared to risk at r∼0) of disease outbreaks that infect at least 3% of the population. Blue line shows best fit of linear regression (confidence interval based on standard error). (C) Relative risk increase (compared to risk at r∼0) of disease outbreaks of a given fraction of the population (on horizontal axis) for two values of assortativity index (r), 0.075 (red) and 0.145 (green). Note that the latter corresponds to r found in the opinionated network (see main text).CC BY · PubMed Central

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Metadata

Authors
Salathé, M., & Khandelwal, S.
Affiliations
Pennsylvania State University (US)
Corresponding author
Salathé, Marcel
ORCID
Salathé, Marcel
Venue
PLoS Computational Biology
Publisher
Public Library of Science (PLoS)
Published
2011-10-13
Volume
7
Issue
10
Pages
e1002199
ISSN
  • 1553-734X (ISSN-L)
  • 1553-7358
DOI
10.1371/journal.pcbi.1002199
PMC
PMC3192813
OpenAlex
W2025330985
Cited by
530
References
29
Citations by year
2026: 5 · 2025: 19 · 2024: 27 · 2023: 30 · 2022: 41 · 2021: 52 · 2020: 53 · 2019: 43 · 2018: 34 · 2017: 44 · 2016: 56 · 2015: 50 · 2014: 35 · 2013: 30 · 2012: 11
FWCI
24.97
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS Computational Biology (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
  • Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
  • arXiv (Cornell University)
Topics
  • Social Sciences › Health › Vaccine Coverage and Hesitancy
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
Keywords
vaccine attitudes, social media, vaccination coverage, infectious disease transmission, outbreak, information flow
MeSH terms
Attitude to Health, Centers for Disease Control and Prevention, U.S., Disease Outbreaks, Humans, Immunization, United States, Social Media

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