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 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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Flagged first, then by citations
Cited by
Sorted by citations
Items 301–400 of 527
Exploring Large Language Models in a Limited Resource Scenario
Panchbhai, A., & Pankanti, S. (2021) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence51648.2021.9377081 · Cited by 11
Prediction model of interaction anxiousness based on Weibo data
Wang, Y., & Zhao, N. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.1045605 · Cited by 9
Network embedding aided vaccine skepticism detection
Béres, F., Michaletzky, T. V., Csoma, R., & Benczúr, A. A. (2023) · Applied Network Science · doi:10.1007/s41109-023-00534-x · Cited by 11
Qasim, R., Farooqui, W. A., Rahman, A., Haroon, R. M., Saleem, M., Rafique, M., Noor, F., Ghani, A., Yaqoob, M., Yadav, U. N., et al. (2023) · BMC Proceedings · doi:10.1186/s12919-023-00265-y · Cited by 10
Twitter (X) in Medicine: Friend or Foe to the Field of Interventional Cardiology?
Rana, M. A., Sorger, G., Cox, D. A., Dangas, G., & Forouzandeh, F. (2023) · Journal of the Society for Cardiovascular Angiography & Interventions · doi:10.1016/j.jscai.2023.101136 · Cited by 10
Kim, E., & Hara, N. (2024) · Sustainability · doi:10.3390/su16041438 · Cited by 11
Building a Learning Marijuana Surveillance System
Levy, S., & Weitzman, E. R. (2016) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/jamapediatrics.2015.3489 · Cited by 9
Rainey, J. J., Kenney, J., Wilburn, B., Putman, A., Zheteyeva, Y. A., & O'Sullivan, M. (2016) · PLoS ONE · doi:10.1371/journal.pone.0163207 · Cited by 9
Mining the vaccination willingness of China using social media data
Ding, J., Wang, A., & Zhang, Q. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104941 · Cited by 9
Hori, A., Wada, K., & SMITH, D. R. (2015) · PLoS ONE · doi:10.1371/journal.pone.0129900 · Cited by 9
Volkova, S., Han, K., & Corley, C. D. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-47880-7_32 · Cited by 9
Measurement Considerations for Quantitative Social Science Research Using Social Media DataPreprint
Ladd, J. M., Ryan, R. M., Singh, L., Bode, L., Budak, C., Conrad, F. G., Cooksey, E. C., Davis-Kean, P. E., Dworak-Fisher, K., Freelon, D., et al. (2020) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/ga6nc · Cited by 10
Identifying emotions in earthquake tweets
Anthony, P., Wong, J. H. K., & Joyce, Z. (2024) · AI & Society · doi:10.1007/s00146-024-02044-5 · Cited by 9
Alahmadi, S., Hoyle, R. B., Head, M., & Brede, M. (2025) · PLoS ONE · doi:10.1371/journal.pone.0318544 · Cited by 8
A Survey on Opinion Dynamics in Social Media Networks: Analysis, Simulation, and Control
Zareer, M. N., & Šelmić, R. R. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3622498 · Cited by 10
How Sentiments Organize Social Action
Shelly, R. K. (2016) · Sociology Compass · doi:10.1111/soc4.12351 · Cited by 8
Sentiment Analysis of COVID-19 Vaccine Rollout in India
Paliwal, S., Parveen, S., Alam, M. A., & Ahmed, J. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-16-5987-4_3 · Cited by 8
Debates about vaccines and climate change on social media networks: a study in contrasts
Schonfeld, J., Qian, E., Sinn, J., Cheng, J., Anand, M., & Bauch, C. T. (2021) · Humanities and Social Sciences Communications · doi:10.1057/s41599-021-00977-6 · Cited by 9
Lekkas, D., Gyorda, J. A., Moen, E. L., & Jacobson, N. C. (2022) · PLoS ONE · doi:10.1371/journal.pone.0277516 · Cited by 8
VaccineWatch: a monitoring system of vaccine messages from social media data
Numnark, S., Ingsriswang, S., & Wichadakul, D. (2014) · IEEE International Conference on Systems Biology · doi:10.1109/isb.2014.6990742 · Cited by 8
Connotative Meanings of Sustainable Mobility: A Segmentation Approach Using Cultural Sentiments
Wolf, I., & Schröder, T. (2019) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2019.06.002 · Cited by 11
Kim, S. Y., Ganesan, K., Dickens, P., & Panda, S. S. (2021) · Sustainability · doi:10.3390/su13052673 · Cited by 19
Metcalf, C. J. E., Andriamandimby, S. F., Baker, R. E., Glennon, E. E., Hampson, K., Hollingsworth, T. D., Klepac, P., & Wesolowski, A. (2021) · Epidemics · doi:10.1016/j.epidem.2021.100507 · Cited by 8
Kim, S., & Real, K. J. (2016) · Health Information & Libraries Journal · doi:10.1111/hir.12132 · Cited by 8
Ponomariov, V., Chirila, L., Apipie, F.-M., Applied Systems srl, C., Abate, R., Rusu, M., Wu, Z., Liehn, E. A., Bucur, I., & Applied Systems srl, C. (2017) · Discoveries · doi:10.15190/d.2017.6 · Cited by 8
A review of media effects: implications for media coverage of air pollution and cancer
Murukutla, N., Kumar, N., & Mullin, S. (2019) · Annals of Cancer Epidemiology · doi:10.21037/ace.2019.07.03 · Cited by 8
Health App Recommendation System using Ensemble Multimodel Deep Learning
Edara, D. C., Sistla, V., & Kolli, V. K. K. (2020) · Journal of Engineering Science and Technology Review · doi:10.25103/jestr.135.03 · Cited by 8
Karami, A., Qiao, Z., Zhang, X., Kharrazi, H. H. K., Bozorgi, P., & Bozorgi, A. (2024) · Big Data and Cognitive Computing · doi:10.3390/bdcc8100130 · Cited by 9
Wu, J., Tanim, S. H., Woo, M., Ahammed, T., Bleichrodt, A. M., & Rennert, L. (2025) · Epidemics · doi:10.1016/j.epidem.2025.100865 · Cited by 10
Measuring Propagation with Temporal Webs
Bramson, A., Hoefman, K., van den Heuvel, M., Vandermarliere, B., & Schoors, K. J. L. (2017) · Theoretical biology · doi:10.1007/978-981-10-5287-3_4 · Cited by 7
Johnson, F. R., Fairchild, A. O., Whittington, D., Srivastava, A. K., González, J. M., & Huang, L. (2022) · Patient · doi:10.1007/s40271-022-00602-x · Cited by 7
The Usage and Impact of ICTs during the Covid-19 Pandemic
Yang, S., Zhu, X. A., & Fichman, P. (2023) · Routledge eBooks · doi:10.4324/9781003231769 · Cited by 10
Sentiment Analysis of Social Media Data on Ebola Outbreak Using Deep Learning Classifiers
Mirugwe, A., Ashaba, C., Namale, A., Akello, E., Bichetero, E., Kansiime, E., & Nyirenda, J. (2024) · Life · doi:10.3390/life14060708 · Cited by 13
Tacken, M. A. J. B., Jansen, B., Mulder, J., Visscher, S., Heijnen, M. A., Campbell, S., & Braspenning, J. C. C. (2012) · Vaccine · doi:10.1016/j.vaccine.2012.12.002 · Cited by 7
Risson, V., Ghodge, B., Bonzani, I. C., Korn, J. R., Medin, J., Saraykar, T., Sengupta, S., Saini, D., & Olson, M. S. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5805 · Cited by 7
Dwiraswati, O., & Siregar, K. N. (2019) · Media Informasi · doi:10.37160/bmi.v15i1.232 · Cited by 7
The Case of Billy Best: 25 Years Later
Marron, J. M., Mithani, Z., & Meyer, E. C. (2021) · PEDIATRICS · doi:10.1542/peds.2020-038299 · Cited by 7
Parenting online: analyzing information provided by parenting-focused Twitter accounts
Ryan, R. M., Davis-Kean, P. E., Bode, L., Krüger, J., Mneimneh, Z., & Singh, L. (2022) · Atlantic Journal of Communication · doi:10.1080/15456870.2022.2061713 · Cited by 7
Ayaz, H., Çelik, M. H., Koytak, H. Z., & Yanik, I. E. (2024) · PLoS ONE · doi:10.1371/journal.pone.0308122 · Cited by 9
Acceptation, refus et hésitation à la vaccination au Canada : défis et approches proposées
Dubé, E. Á., Bettinger, J., Fisher, W., Nauš, M., Mahmud, S., & Hilderman, T. (2016) · Relevé des maladies transmissibles au Canada · doi:10.14745/ccdr.v42i12a02f · Cited by 6
Lappeman, J., Munyai, K., & Kagina, B. M. N. (2021) · F1000Research · doi:10.12688/f1000research.52061.1 · Cited by 7
Spreading (dis)trust in Fiji? Exploring COVID-19 misinformation on Facebook forums
Kant, R., & Varea, R. (2021) · Pacific Journalism Review – Te Koakoa · doi:10.24135/pjr.v27i1and2.1166 · Cited by 8
Liu, X., Yu, W., Yin, Z., Rodewald, L. E., Song, Y., Zhang, Z., Ye, J., Li, L., Cao, L., & Cao, L. (2022) · Vaccine · doi:10.1016/j.vaccine.2022.03.035 · Cited by 6
Gai, Z., Fan, C., Shen, S., Ge, Y., Shi, Z., Li, S., Zhang, Y., & Cao, Y. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/1456382 · Cited by 6
RetractedPark, J., Tsou, M., Nara, A., Cassels, S. L., & Dodge, S. (2024) · Urban Informatics · doi:10.1007/s44212-023-00033-5 · Cited by 8
Text Mining of PeduliLindungi Application Reviews on Google Play Store
Saputra, I., Djatna, T., Siregar, R. R. A., Kristiyanti, D. A., Yani, H. R., & Riyadi, A. A. (2022) · Faktor Exacta · doi:10.30998/faktorexacta.v15i2.10629 · Cited by 6
Zhang, H., Wheldon, C. W., Tao, C., Dunn, A. G., Guo, Y. M., Huo, J. S., & Bian, J. (2019) · Springer eBooks · doi:10.1007/978-3-030-14714-3_11 · Cited by 6
Jiao, J., Suarez, G. P., & Fefferman, N. H. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008762 · Cited by 6
Deep learning modeling of public’s sentiments towards temporal evolution of COVID-19 transmission
Wang, Y., Chew, A. W. Z., & Zhang, L. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109728 · Cited by 6
Alessi, M., Camillò, A., Chetta, V., Giangreco, E., Soufivand, M., & Storelli, D. (2015) · Complex Systems Informatics and Modeling Quarterly · doi:10.7250/csimq.2015-5.03 · Cited by 6
Detecting and Extracting of Adverse Drug Reaction Mentioning Tweets with Multi-Head Self Attention
Ge, S., Qi, T., Wu, C., & Huang, Y. (2019) · Social Media Mining for Health Research and Applications Workshop and Shared Tasks (SMM4H) · doi:10.18653/v1/w19-3214 · Cited by 6
COVID-19 vaccine perceptions: An observational study on RedditPreprint
Kumar, N., Corpus, I., Hans, M., Harle, N., Yang, N., McDonald, C., Sakai, S. N., Janmohamed, K., Tang, W., Schwartz, J. L., et al. (2021) · medRxiv · doi:10.1101/2021.04.09.21255229 · Cited by 6
Ali, G. G. M. N., Rahman, M. M., Hossain, A., Rahman, M. S., Paul, K. C., Thill, J., & Samuel, J. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3849138 · Cited by 6
Horowitz, R., Athanasiadou-Lewis, C., & Zervoulis, K. (2025) · The European Journal of Counselling Psychology · doi:10.46853/001c.141519 · Cited by 6
Dynamics of Social Media Promotion of Prescription Drugs and Resulting Patient Belief Systems
Mukherjee, S. K., Kumar, J., Jha, A. K., & Pandey, J. R. (2021) · Advances in social networking and online communities book series · doi:10.4018/978-1-7998-7764-6.ch005 · Cited by 5
Overcoming Vaccine Hesitancy by Multiplex Social Network Targeting
Fügenschuh, M., & Fu, F. (2023) · Studies in computational intelligence · doi:10.1007/978-3-031-21127-0_47 · Cited by 5
Unconventional data, unprecedented insights: leveraging non-traditional data during a pandemic
Bolt, K., Gil‐González, D., & Oliver, N. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1350743 · Cited by 8
Nelson, V., Bashyal, B., Tan, P., & Argyris, Y. A. (2024) · Social Science & Medicine · doi:10.1016/j.socscimed.2024.116775 · Cited by 5
Twitter data analysis to understand societal response to air quality
Gurajala, S., & Matthews, J. N. (2018) · International Conference on Social Media and Society (SMSociety) · doi:10.1145/3217804.3217900 · Cited by 4
Bodrunova, S. S., Blekanov, I. S., Kukarkin, M., & Zhuravleva, N. N. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01437-7_18 · Cited by 5
Zivich, P. N., Volfovsky, A., Moody, J. W., & Aiello, A. E. (2021) · American Journal of Epidemiology · doi:10.1093/aje/kwab167 · Cited by 5
Westmaas, J. L., Masters, M., Bandi, P., Majmundar, A., Asare, S., & Diver, W. R. (2022) · JMIR Infodemiology · doi:10.2196/36215 · Cited by 4
Lokmanoglu, A. D., Nisbet, E. C., Osborne, M. T., Tien, J. H., Malloy, S., Chacón, L. M. C., Villa-Turek, E., & Abhari, R. (2023) · Vaccines · doi:10.3390/vaccines11040817 · Cited by 5
LaCour, M., Hughes, B., Goldwater, M. B., Ireland, M. E., Worthy, D. A., Allen, J. V., Gaylord, N., Van‐Hoosier, G., & Davis, T. H. (2021) · Risk Analysis · doi:10.1111/risa.13764 · Cited by 5
How SES May Figure in Perceptions of Zika Risks and in Preventive Action
Lo, C. C., & Laurent‐Simpson, A. (2018) · Sociological Spectrum · doi:10.1080/02732173.2018.1502109 · Cited by 5
Social networks and popular understanding of science and health: Sharing disparities
Robbins, R. (2014) · Journal of Communication · doi:10.1111/jcom.12119 · Cited by 5
(2020) · SAGE Research Methods Foundations · doi:10.4135/9781526421036883648 · Cited by 5
Wang, Y., Basellini, U., & Camarda, C. G. (2025) · Social Science & Medicine · doi:10.1016/j.socscimed.2025.118881 · Cited by 4
Ansah, P. (2022) · Journal of International Crisis and Risk Communication Research · doi:10.30658/jicrcr.5.1.3 · Cited by 4
Social inequalities in vaccine coverage and their effects on epidemic spreading
Manna, A., Karsai, M., & Perra, N. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013585 · Cited by 3
Improving Human Papillomavirus Vaccine Uptake
Brown, B. J., Hopfer, S., & Chan, A. Y. (2015) · Californian Journal of Health Promotion · doi:10.32398/cjhp.v13i2.1818 · Cited by 4
Europäische Diskursgemeinschaft.
Atayan, V., Felder, E., Fetzer, B., Mattfeldt, A., Moretti, D., Straube, A., & Wachter, D. (2020) · Linguistik Online · doi:10.13092/lo.103.7113 · Cited by 4
Investigating #vapingcessation in Twitter
Amin, S., Jaiswal, A., Washington, P. Y., & Pokhrel, P. (2023) · American Journal of Health Behavior · doi:10.5993/ajhb.47.6.11 · Cited by 4
Big Data For Health Through Social Media
Mayer, M. Á., Fernández-Luque, L., & Leis, Á. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-809269-9.00005-0 · Cited by 4
Public Health Intelligence and the Internet: Current State of the Art
Shin, E. K., & Shaban‐Nejad, A. (2017) · Lecture notes in social networks · doi:10.1007/978-3-319-68604-2_1 · Cited by 4
A stochastic simulation model to study respondent-driven recruitment
Stein, M. L., Buskens, V., van der Heijden, P. G. M., van Steenbergen, J., Wong, A., Bootsma, M., & Kretzschmar, M. (2018) · PLoS ONE · doi:10.1371/journal.pone.0207507 · Cited by 4
Kostkova, P. (2015) · Cambridge University Press eBooks · doi:10.1017/cbo9781316182635.007 · Cited by 4
Extracting Social Structures from Conversations in Twitter
Al-Rubaye, A., & de Menezes, R. P. (2016) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/2914586.2914599 · Cited by 4
Crowdbreaks: Tracking Health Trends using Public Social Media Data and CrowdsourcingPreprint
Mueller, M., & Salathé, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.05491 · Cited by 4
Self-citationModeling Considerations for Quantitative Social Science Research Using Social Media DataPreprint
Budak, C., Soroka, S., Singh, L., Bailey, M., Bode, L., Chawla, N. V., Davis-Kean, P. E., Choudhury, M. D., Veaux, R. D. D., Hahn, U., et al. (2021) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/3e2ux · Cited by 4
Tracking the Diffusion of Disinformation on the SDGs Across Social Media Platforms
Abdullahi, B. S., & Pindiga, H. I. (2023) · Sustainable development goals series · doi:10.1007/978-3-031-19459-7_6 · Cited by 3
한창수, Ki, M., Yang, J., Na, C., Kim, J.-S., Yu, H., Ki, M., Lee, M.-S., & Jeong, H.-G. (2024) · Social Science & Medicine · doi:10.1016/j.socscimed.2024.117276 · Cited by 3
Microbial genomics and infectious disease forecasting
Desai, S. A., Patel, V. P., Nagare, S. D., Patil, R., & More, M. J. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-31554-1.00012-1 · Cited by 3
COVID Vaccine Sentiment Dashboard based on Twitter Data
Béres, F., Csoma, R., Michaletzky, T. V., & Benczúr, A. A. (2022) · Scientia et Securitas · doi:10.1556/112.2021.00066 · Cited by 3
Ferreira-Silva, S. N., Soares, M. E. M., Vasconcelos, R. D. P., Barbieri, C., Fujita, L., Medeiros, T., Matos, C. C. D. S. A., Couto, M. T., & Avelino‐Silva, V. I. (2024) · Vaccine · doi:10.1016/j.vaccine.2024.126247 · Cited by 3
You, S., Chen, S., & Liao, C. (2018) · Infection and Drug Resistance · doi:10.2147/idr.s153797 · Cited by 3
Viral Dynamics and Mathematical Models
Arinaminpathy, N., Metcalf, C. J. E., & Grenfell, B. T. (2014) · Springer eBooks · doi:10.1007/978-1-4899-7448-8_5 · Cited by 3
Leveraging Social Media Towards Understanding Anti-Vaccination Campaigns
Bhattacharjee, U., Srijith, P. K., & Desarkar, M. S. (2019) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets.2019.8711365 · Cited by 3
Challenges in the use of social media data for the next generation analyst
Grace, R., & Leskovich, R. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2177834 · Cited by 3
Edet, R., & Bolarinwa, O. A. (2019) · Advanced Journal of Social Science · doi:10.21467/ajss.6.1.57-63 · Cited by 3
Health Information and the Like
Reddi, A. H. (2013) · Science · doi:10.1126/science.342.6164.1315-a · Cited by 3
Infoveillance to Analyze Covid19 Impact on Central America PopulationPreprint
Saire, J. E. C., & Lemus-Martin, R. (2020) · medRxiv · doi:10.1101/2020.05.26.20113514 · Cited by 3
Monitoring stance towards vaccination in twitter messages
Kunneman, F., Lambooij, M., Wong, A., van den Bosch, A. P. J., & Mollema, L. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-1046-y · Cited by 2
Ajovalasit, S., Dorgali, M. V., Mazza, A., d’Onofrio, A., & Manfredi, P. (2021) · PLoS ONE · doi:10.1371/journal.pone.0253569 · Cited by 23
Interplay of epidemic spreading and vaccine uptake under complex social contagion
de Miguel-Arribas, A., Aleta, A., & Moreno, Y. (2025) · Physical review. E · doi:10.1103/wmq3-229x · Cited by 2
Social imitation dynamics of vaccination driven by vaccine effectiveness and beliefs
Fu, F., Zhuo, R., & Chen, X. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013586 · Cited by 2
Leveraging Social Media to Track Urban Park Quality for Improved Citizen Health
Dony, C. C., & Fekete, E. (2019) · Global perspectives on health geography · doi:10.1007/978-3-030-19573-1_9 · Cited by 2
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. (2023) · F1000Research · doi:10.12688/f1000research.130610.1 · Cited by 3
Tracking the structure and sentiment of vaccination discussions on Mumsnet
Silva, M., Skeva, R., House, T., & Jay, C. (2023) · Social Network Analysis and Mining · doi:10.1007/s13278-023-01155-z · Cited by 2
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review