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Assessing Vaccination Sentiments with Online Social Media: Implications for Infectious Disease Dynamics and Control
Open accessAbstract
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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“Previous work around similar disorders suggests that social media use may contribute to an ‘echo-chamber’ effect, where users perceive their values and world-views to be more common than they actually are, due to selectively viewing contributions of other, similarly minded people [5].”
From Instagram use is linked to increased symptoms of orthorexia nervosa. (2017) · cites this paper as [5]
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Cites
Infectious Diseases of Humans: Dynamics and Control
(1992) · Annals of Internal Medicine · doi:10.7326/0003-4819-117-2-174_4 · Cited by 7,819
Actual causes of death in the United States
McGinnis, J. M. (1993) · JAMA: The Journal of the American Medical Association · doi:10.1001/jama.1993.03510180077038 · Cited by 5,480
Actual Causes of Death in the United States, 2000
Mokdad, A. H. (2004) · JAMA · doi:10.1001/jama.291.10.1238 · Cited by 5,211
The Spread of Obesity in a Large Social Network over 32 Years
Christakis, N. A., & Fowler, J. H. (2007) · New England Journal of Medicine · doi:10.1056/nejmsa066082 · Cited by 5,007
Detecting influenza epidemics using search engine query data
Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009) · Nature · doi:10.1038/nature07634 · Cited by 4,295
Natural language processing with Python
Bird, S., Klein, E., & Loper, E. (2009) · CERN Document Server (European Organization for Nuclear Research) · Cited by 3,472
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., et al. (2009) · Science · doi:10.1126/science.1167742 · Cited by 3,365
Newman, M. (2002) · Physical review. E, Statistical, nonlinear, and soft matter physics · doi:10.1103/physreve.67.026126 · Cited by 3,130
The spread of behavior in an online social network experiment
Centola, D. (2010) · Science · doi:10.1126/science.1185231 · Cited by 2,692
The collective dynamics of smoking in a large social network
Christakis, N. A., & Fowler, J. H. (2008) · New England Journal of Medicine · doi:10.1056/nejmsa0706154 · Cited by 2,390
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Fundamentación ética del deber sobre el acceso abierto a fuentes de información científica
Valverde, A., & González-Miranda, J. A. (2024) · Revista Espiga · doi:10.22458/re.v23i47.5136 · Cited by 2
Theoretical perspectives on the use of social media in the propagation of health messages
McNeill, A. R. (2014) · Cited by 2
Complex social contagion makes networks more vulnerable to disease
Salathé, M. (2013) · Cited by 2
Self-citationXiong, Y., He, Y., Huang, H., Yu, C., & Jing, X. (2020) · Lecture notes in electrical engineering · doi:10.1007/978-981-15-4163-6_10 · Cited by 2
COVID-19 Pandemic: Health Communication, One Health and Biosecurity
MADEN, M. (2020) · Turkiye Klinikleri Journal of Veterinary Sciences · doi:10.5336/vetsci.2020-76210 · Cited by 2
Collecting Domain-Relevant Tweets: Creation and Application of a New Framework
Câmpan, A., Truţă, T. M., Huesman, S., Wardrop, C., & Appelman, A. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9377778 · Cited by 2
Sentiment Analysis for Twitter Chatter During the Early Outbreak Period of COVID-19
Jubair, F., Salim, N. A., Al‐karadsheh, O., Hassona, Y. M., Saifan, R., & Abdel-Majeed, M. (2021) · International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) · doi:10.1109/isriti54043.2021.9702837 · Cited by 2
Exploring Sentiment Analysis on Arabic Tweets about the COIVD-19 Vaccines
Alsabban, W. H. (2022) · Tehnički glasnik · doi:10.31803/tg-20220124144912 · Cited by 2
Conceptualizing an Ecological Model of Google Search and Twitter Data in Public Health
Liang, B., & Wang, Y. (2021) · Human dynamics in smart cities · doi:10.1007/978-3-030-83010-6_10 · Cited by 2
Towards Sentiment Analysis of Tobacco Products’ Usage in Social Media
Yanamandra, V. H., Pant, K., & Mamidi, R. (2021) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-072-4_173 · Cited by 2
Khan, I. U., Aslam, N., Chrouf, S. M. B., Atef, I., Merah, I., Almulhim, L. A. A., & AlShuaifan, R. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/6722427 · Cited by 2
Cultural Event Management and Urban E-Planning Through Bottom-Up User Participation
Corallo, A., Trono, A., Fortunato, L., Pettinato, F., & Schina, L. (2018) · IGI Global eBooks · doi:10.4018/978-1-5225-5646-6.ch050 · Cited by 2
Targeting HIV-related Medication Side Effects and Sentiment Using Twitter DataPreprint
Adrover, C., Bodnar, T. J., & Salathé, M. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1404.3610 · Cited by 2
Self-citationYouth and Online News: Reflections and PerspectivesPreprint
Cortesi, S., Gasser, U., Genner, S., Graeff, E., Haduong, P., Reynol Junco, A., Murillo, L. F. R., Othman, D., Rosenberg, G., Robinson, E., et al. (2015) · SSRN Electronic Journal · doi:10.2139/ssrn.2566446 · Cited by 2
Becker, B. F. H. (2019) · RePub (Erasmus University Rotterdam) · Cited by 2
O IMPACTO DAS FAKE NEWS NA VACINAÇÃO E NOS SURTOS DE DOENÇAS ERRADICADAS
Ferreira, D. A., da Silva, A. P., & Montenegro, C. D. A. (2021) · Revista interdisciplinar em saúde · doi:10.35621/23587490.v8.n1.p2-16 · Cited by 1
Donar, G. B. (2020) · Türkiye Halk Sağlığı Dergisi · doi:10.20518/tjph.656035 · Cited by 1
Privacy in Online Social Networks: A Systematic Mapping Study and a Classification Framework
Bouraga, S., Jureta, I., & Faulkner, S. (2022) · Lecture notes in social networks · doi:10.1007/978-3-031-07869-9_6 · Cited by 1
Alsulaiman, S. A. (2023) · Online Journal of Communication and Media Technologies · doi:10.30935/ojcmt/13783 · Cited by 1
Русских, С. В., Тарасенко, Е. А., Kaunina, D. V., Gorbacheva, N. А., Васильев, М. Д., Загдын, З. М., Drobizhev, М. Y., & Rotov, V. M. (2024) · Сибирский научный медицинский журнал · doi:10.18699/ssmj20240221 · Cited by 1
Hidalgo, P. J. J., Ruíz-Núñez, C., Jiménez-Gómez, B., Segado-Fernández, S., Romero-Magdalena, C. S., López‐Espuela, F., & Herrera‐Peco, I. (2024) · Social Sciences · doi:10.3390/socsci13100557 · Cited by 1
Dynamical evolution of social network polarization and its impact on the propagation of a virus
Achitouv, I., & Chavalarias, D. (2025) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2025.116676 · Cited by 1
Alahmadi, S., Hoyle, R. B., Head, M., & Brede, M. (2025) · Applied Network Science · doi:10.1007/s41109-025-00718-7 · Cited by 1
Homophily in a multi-group vaccination game and its impact on disease dynamics
He, Z., Manfredi, P., d’Onofrio, A., & Bauch, C. T. (2026) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2025.2530 · Cited by 1
Tweet Classification Framework for Detecting Events Related to Health Problems
Majak, M., Żołnierek, A., Węgrzyn-Wolska, K. M., & Bougueroua, L. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-59162-9_47 · Cited by 1
Payday Loan Marketing in Social Media Networks
Hao, J., & Dai, H. D. (2017) · Journal of Consumer Affairs · doi:10.1111/joca.12151 · Cited by 1
Geovisual analytics approaches for the integration of geography and social network contexts
Luo, W. P. (2014) · Cited by 1
Social media insights for sustainable development and humanitarian action in Indonesia
Amin, I., Pramestri, Z. A., Hodge, G., & Lee, J. G. (2018) · Journal of Physics Conference Series · doi:10.1088/1742-6596/971/1/012040 · Cited by 1
Utilising Social Media for Intervening and Predicting Future Health in Societies
Laaksonen, C., Jalonen, H., & Paavola, J. (2014) · Communications in computer and information science · doi:10.1007/978-3-319-10211-5_11 · Cited by 1
Role and Usage of Social Media in COVID-19
Singh, A., Mukherjee, S. K., Pandey, V., & Jha, A. K. (2022) · International Journal of e-Collaboration · doi:10.4018/ijec.295147 · Cited by 1
Health Talk: Understanding Practices of Popular Professional YouTubers
Phan, T., Michoud, C., Volpato, L., Carral, M. D. R., & Gática-Pérez, D. (2022) · International Conference on Mobile and Ubiquitous Multimedia (MUM) · doi:10.1145/3568444.3568453 · Cited by 1
A multiscale modeling framework to study the interdependence of brain, behavior, and pandemic
Kumar, S.-J. S., Sharma, B., & Singh, V. (2023) · Nonlinear Dynamics · doi:10.1007/s11071-022-08204-w · Cited by 1
Chen, X. K., Ong, J., Neo, L. S., & Tan, L. F. (2023) · Translational Behavioral Medicine · doi:10.1093/tbm/ibac095 · Cited by 1
Association between Obesity and COVID-19: Insights from Social Media Content
Alotaibi, M., Pai, R. R., Alathur, S., Chetty, N., Alhmiedat, T. A., Aborokbah, M. M., Albalawi, U., Marie, A., Bushnag, A., & Kumar, V. (2023) · Information · doi:10.3390/info14080448 · Cited by 1
Sentiment analysis of Indonesian tweets on COVID-19 and COVID-19 vaccinations
Kalanjati, V. P., Hasanatuludhhiyah, N., d’Arqom, A., Arsyi, D. H., Marchianti, A. C. N., Muhammad, A., & Purwitasari, D. (2024) · F1000Research · doi:10.12688/f1000research.130610.3 · Cited by 1
Shah, A., Shah, S. R., Rand, B., & Champon, X. (2024) · Journal of the Southern Association for Information Systems · doi:10.17705/3jsis.00034 · Cited by 1
Optimal Intervention Campaign to Combat Anti-vaccine Social Contagion and Contain Epidemic Spread
Alahmadi, S., Hoyle, R. B., Head, M., & Brede, M. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-82439-5_8 · Cited by 1
A Semantic Web Platform for Online Vaccine Sentiment Surveillance
Shaban‐Nejad, A., Menon, S., & Buckeridge, D. L. (2015) · Online Journal of Public Health Informatics · doi:10.5210/ojphi.v7i1.5823 · Cited by 1
KUNLPLab:Sentiment Analysis on Twitter Data
Assefa, B. G. (2014) · International Workshop on Semantic Evaluation (SemEval) · doi:10.3115/v1/s14-2067 · Cited by 1
Chitanvis, M. E., Daughton, A. R., Altherr, F. M., Parikh, N., Fairchild, G. C., Rosenberger, W. E., Velappan, N., Hollander, A., Lyon, E. Z. A., Vuyisich, G., et al. (2019) · Health Security · doi:10.1089/hs.2019.0020 · Cited by 1
Daria, T., & Larson, H. J. (2019) · International Conference on Digital Health · doi:10.1145/3357729.3357763 · Cited by 1
Predicting Collective Synchronous State of Sentiments for Users in Social Media
Khan, N. S., & Ghani, M. S. (2019) · Mehran University Research Journal of Engineering and Technology · doi:10.22581/muet1982.1903.13 · Cited by 1
McAndrew, T., Bongard, J., Danforth, C. M., Dodds, P. S., Hines, P., & Bagrow, J. P. (2016) · Advances in Social Networks Analysis and Mining · doi:10.5555/3192424.3192521 · Cited by 1
Design of Social Isolation Index
Ohki, Y., Ikeda, Y., & Iyetomi, H. (2021) · Springer eBooks · doi:10.1007/978-981-15-4944-1_13 · Cited by 1
Sentimental Analysis for Studying and Analyzing the Spreading of COVID-19 from Twitter Data
Baker, Q. B., Aqouleh, A. A., & Altiti, O. (2021) · International Conference on Social Networks Analysis, Management and Security (SNAMS) · doi:10.1109/snams53716.2021.9731855 · Cited by 1
Comunicación en redes sociales en escenarios de pandemia o epidemia: un análisis bibliométrico
Andrade, C. F. O., Pastrana, C. A. A., & Jiménez‐Zarco, A. I. (2021) · Revista Perspectiva Empresarial · doi:10.16967/23898186.742 · Cited by 1
Cai, R., Zhang, J., Li, Z., Zeng, C., Qiao, S., & Li, X. (2022) · JMIR Formative Research · doi:10.2196/37582 · Cited by 2
Jurnalism științific. Cum a schimbat pandemia de Covid 19 raportul dintre jurnalism și știință?
Sălcudean, M. (2022) · Transilvania · doi:10.51391/trva.2022.05.03 · Cited by 1
Topic Modeling with Structured Priors for Text-Driven Science
Paul, M. J. (2015) · Cited by 1
Liu, Y. (2016) · Deep Blue (University of Michigan) · Cited by 1
Irrationality, Misinformation and the Role of Science Education in Science Communication.
Sparrow, A. (2017) · Otago University Research Archive (University of Otago) · Cited by 1
Wang, M. (2017) · Spectrum Research Repository (Concordia University) · Cited by 1
Shah, Z., Martin, P. B., Coiera, E. W., Mandl, K. D., & Dunn, A. G. (2018) · arXiv (Cornell University) · Cited by 1
Ayaz, H., Çelik, M. H., Koytak, H. Z., & Yanik, I. E. (2023) · medRxiv · doi:10.1101/2023.07.24.23293093 · Cited by 1
Herd immunity and measles: why we should aim for 100% vaccination coverage
Salathé, M. (2015) · doi:10.64628/aai.3v3pd56k6 · Cited by 1
Self-citationMicro-text classification between small and big data
Christen, M., Niederberger, T., Ott, T., Aryobsei, S., & Hofstetter, R. (2015) · Nonlinear Theory and Its Applications IEICE · doi:10.1587/nolta.6.556
Public behaviour and the response to pandemic influenza
Castro‐Sánchez, E., & Holmes, A. H. (2012) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(12)70244-x
IITPatna: Supervised Approach for Sentiment Analysis in Twitter
Selvarajan, R., & Ekbal, A. (2014) · International Workshop on Semantic Evaluation (SemEval) · doi:10.3115/v1/s14-2054
Public behaviour and the response to pandemic infl uenza
Holmgren, J. R. (2012)
Indian Institute of Technology-Patna: Sentiment Analysis in Twitter
Singh, V., Khan, A. M., & Ekbal, A. (2014) · International Workshop on Semantic Evaluation (SemEval) · doi:10.3115/v1/s14-2057
Data science and spatio-temporal resolved social networks
Mocanu, D. (2014) · Digital Repository Service (Northeastern University) · doi:10.17760/d20005098
McAndrew, T. C., Bongard, J., Danforth, C. M., Dodds, P. S., Hines, P., & Bagrow, J. P. (2016) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2016.7752284
Scanning the Science-Society Horizon
Moon, B. (2016) · ANU Open Research (Australian National University) · doi:10.25911/5d6664e8354b8
Risson, V., Godge, B., Bonzani, I. C., Korn, J. R., Medin, J., & Olson, M. S. (2016) · Value in Health · doi:10.1016/j.jval.2016.03.651
LSC 2016: from system approaches in lung disease to getting the job you want
Boots, A. W., Flood, E., Kahn, N. C., Hardavella, G. F., Bikov, A., Aamli, A., & Skoczyński, S. (2016) · Breathe · doi:10.1183/20734735.006816
Early Warning Signals of Vaccine Scares.
Pananos, A. D. (2016) · UWSpace (University of Waterloo)
Every tweet counts: Examining spatial variability of twitter data representativeness
Omri, M. (2016) · Scholars' Bank (University of Oregon)
Liu, Y., O'Brien, T., Sondhi, E., Mei, Q., Hanauer, D. A., & Zheng, K. (2015)
The Internet and Childhood Immunizations in Canada
Tustin, J. L. (2016) · TSpace (University of Toronto)
Overview of Big Data in Healthcare
Zarandi, M. H. F., & Gamasaee, R. (2017) · Advances in healthcare information systems and administration book series · doi:10.4018/978-1-5225-2515-8.ch016
Disease Spreading in Time-Evolving Networked Communities
Pacheco, J. M., Segbroeck, S. V., & Santos, F. C. (2017) · Theoretical biology · doi:10.1007/978-981-10-5287-3_13
The Design of Interactive Visualizations and Analytics for Public Health Data
Ola, O. (2017) · Scholarship@Western (Western University)
Real Time Opinion Mining and Analysis of Twitter Data
Kumar, K. S., Trumboo, M. M., Vaibhav, & Ahlawat, S. (2018) · International Journal of Engineering & Technology · doi:10.14419/ijet.v7i3.12.16104
Increasing Herd Immunity with Influenza RevaccinationPreprint
Mooring, E. Q., & Bansal, S. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1308.5365
An appraisal of health datasets to enhance the surveillance of Lyme disease in the United Kingdom
Tulloch, J. (2019) · University of Liverpool · doi:10.17638/03046553
Quantifying uncertainty in population weighting of Twitter analyses for urban risk assessment
Bastian, E., Myers, N., Ehlschlaeger, C., & Burkhalter, J. A. (2019) · doi:10.21079/11681/34163
Population outreach program in controlling COVID-19 through social media
Ali, S. (2020) · Amrita Journal of Medicine · doi:10.4103/amjm.amjm_26_20
Complex Network Dynamics in Population Health
Moody, J. W., & Pasquale, D. K. (2020) · Oxford University Press eBooks · doi:10.1093/oso/9780190880743.003.0004
Hawks, J. R., Nara, A., Wells, K. J., Ferrand, J. L., & Buhi, E. R. (2020) · Health · doi:10.4236/health.2020.129081
Flahault, A. (2021) · Springer eBooks · doi:10.1007/978-3-030-05325-3_70-1
Understanding the Effect of Social Media
Liu, J., & Xia, S. (2020) · Health information science · doi:10.1007/978-3-030-52109-7_6
Biswas, M. R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.11385
Social Media Affects Health Behavior
Meenakshi, S. (2021) · IARJSET · doi:10.17148/iarjset.2021.8866
Social physicsPreprint
Jusup, M., Holme, P., Kanazawa, K., Takayasu, M., Romić, I., Wang, Z., Geček, S., Lipić, T., Boris Podobnik, A., Wang, L., et al. (2021) · arXiv (Cornell University)
Wijaya, K. P., Götz, T., Ganegoda, N. C., Rockenfeller, R., Aldila, D., Schäfer, M., & Jayathunga, Y. (2022) · doi:10.54103/milanoup.73.71
Flahault, A. (2021) · Springer eBooks · doi:10.1007/978-3-030-45009-0_70
Role of Social Media Promotion of Prescription Drugs on Patient Belief-system and Behaviour
Mukherjee, S. K., Kumar, J., Jha, A. K., & Rani, J. (2021) · IGI Global eBooks · doi:10.4018/978-1-6684-2414-8.ch019
Weston, D., Bloodworth, N., Amlôt, R., & Rubin, G. J. (2019) · doi:10.2196/preprints.13918
Wu, P., Li, S., Liu, Z., Wang, Y., Liu, T., Liu, X., Zhao, N., & Zhu, T. (2020) · doi:10.2196/preprints.20452
Cultural Event Management and Urban E-Planning Through Bottom-Up User Participation
Corallo, A., Trono, A., Fortunato, L., Pettinato, F., & Schina, L. (2018) · IGI Global eBooks · doi:10.4018/978-1-5225-7030-1.ch046
COVID-19 Vaccine Brand Sentiment on Twitter
Câmpan, A., Truţă, T. M., Huesman, S., Meda, V., & Anderson, J. (2022) · doi:10.1145/3524010.3539496
Studying Public Perception about Covaxin Vaccination
Chinnaiyan, S., Govindaraj, Y. K., Dharmaraj, A., & Babu, B. (2022) · Journal of Public Health and Primary Care · doi:10.4103/jphpc.jphpc_13_21
Kumar, K., & Pande, B. P. (2022) · Lecture notes in social networks · doi:10.1007/978-3-031-07869-9_1
Public access to public health information
Kevany, S., Kirk, A., & Canyon, D. V. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-91705-6.00003-4
Rahman, S. M., Mamoon, M., Haque, A. B., Abedin, M. J., Haque, R., Nahar, R., Uddin, M. N., & Islam, M. S. (2023) · Springer eBooks · doi:10.1007/978-981-19-7299-7_18
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
- 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
- gold
- doi.org
- 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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