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

By Marcel Salathé, Shashank Khandelwal. PLoS Comput. Biol., 2011

541 citations recorded by the source corpus.

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.

Subfields: Epidemiology, Health, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/6488267e3675fea943d23c0df56447db2364ddc9

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Citations in the corpus

NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets (2013, background): “, 2009), health (Chew and Eysenbach, 2010; Salathé and Khandelwal, 2011), and disaster management (Verma et al.”
Digital Epidemiology (2012, background): “…rapid detection of an unusual respiratory illness in a remote village anywhere on the globe [5], the near real-time estimation of influenza activity levels [6,7], and the assessment of vaccination sentiments during pandemic preparedness efforts [8] are examples of realizations of this potential.”
Coupled disease–behavior dynamics on complex networks: A review (2015, background): “analyzed the spatiotemporal individuals’ sentiments towards the novel influenza A (H1N1) vaccine [97].”
A New Dimension of Health Care: Systematic Review of the Uses, Benefits, and Limitations of Social Media for Health Communication (2013, background): “The 98 selected studies are summarized by study design, social media tool/application, study purpose, participants/sample and sample size, measurement tools, results, conclusion, and use of social media in Multimedia Appendix 2 [24-121].”
Statistical physics of vaccination (2016, background): “The aforementioned studies have proved that positively assortative non-vaccinated individuals in contact networks can lead to an enlarged likelihood of large disease outbreaks [432, 575, 581].”
Regular Article (2004, unknown): “that behaviors are often clustered (4, 5) and positively assorted at the dyadic level (6, 7) - can be explained by multiple processes, the two most prominent being homophily and social influence.”
Modeling infectious disease dynamics in the complex landscape of global health (2015, unknown): “Analysis of data from online social networks has also created promising opportunities to validate such approaches with empirical observations (47, 48).”
Systematic Literature Review on the Spread of Health-related Misinformation on Social Media (2019, background): “This has generated a series of studies of information diffusion (Serrano et al., 2015) , rumour spread (He et al., 2015) , and consequent behavioural changes (Salathé and Khandelwal, 2011; Wakamiya et al., 2016) .”