{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T21:16:05Z","timestamp":1780434965822,"version":"3.54.1"},"reference-count":89,"publisher":"Springer Science and Business Media LLC","issue":"1-2","license":[{"start":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T00:00:00Z","timestamp":1655337600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T00:00:00Z","timestamp":1655337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Ann Oper Res"],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1007\/s10479-022-04792-3","type":"journal-article","created":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T20:02:41Z","timestamp":1655409761000},"page":"477-515","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["COVID-19 vaccine hesitancy: a social media analysis using deep learning"],"prefix":"10.1007","volume":"339","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6745-7417","authenticated-orcid":false,"given":"Serge","family":"Nyawa","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6752-4269","authenticated-orcid":false,"given":"Dieudonn\u00e9","family":"Tchuente","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1073-058X","authenticated-orcid":false,"given":"Samuel","family":"Fosso-Wamba","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,6,16]]},"reference":[{"key":"4792_CR1","doi-asserted-by":"crossref","unstructured":"Abd Rahim, N. H., & Rafie, S. H. M. (2020). Sentiment analysis of social media data in vaccination. International Journal, 8(9).","DOI":"10.30534\/ijeter\/2020\/60892020"},{"key":"4792_CR2","doi-asserted-by":"crossref","unstructured":"Akyildirim E., Corbet S., Efthymiou M., Guiomard C., O\u2019Connell J., & Sensoy A. (2020). The financial market effects of international aviation disasters. International Review of Financial Analysis, 69, 101468.","DOI":"10.1016\/j.irfa.2020.101468"},{"key":"4792_CR3","volume":"239","author":"ST Alam","year":"2021","unstructured":"Alam, S. T., Ahmed, S., Ali, S. M., Sarker, S., & Kabir, G. (2021). Challenges to COVID-19 vaccine supply chain: Implications for sustainable development goals. International Journal of Production Economics, 239, 108193.","journal-title":"International Journal of Production Economics"},{"key":"4792_CR4","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.104957","volume":"139","author":"AH Alamoodi","year":"2021","unstructured":"Alamoodi, A. H., Zaidan, B. B., Al-Masawa, M., Taresh, S. M., Noman, S., Ahmaro, I. Y. Y., Garfan, S., Chen, J., Ahmed, M. A., Zaidan, A. A., Albahri, O. S., Aickelin, U., Thamir, N. N., Fadhil, J. A., & Salahaldin, A. (2021). Multi-perspectives systematic review on the applications of sentiment analysis for vaccine hesitancy. Computers in Biology and Medicine, 139, 104957.","journal-title":"Computers in Biology and Medicine"},{"issue":"1","key":"4792_CR5","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/s10479-016-2392-0","volume":"283","author":"A Anparasan","year":"2019","unstructured":"Anparasan, A., & Lejeune, M. (2019). Resource deployment and donation allocation for epidemic outbreaks. Annals of Operations Research, 283(1), 9\u201332.","journal-title":"Annals of Operations Research"},{"key":"4792_CR6","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511624216","volume-title":"Neural Network Learning: Theoretical Foundations","author":"M Anthony","year":"1999","unstructured":"Anthony, M., & Bartlett, P. L. (1999). Neural Network Learning: Theoretical Foundations. Cambridge University Press."},{"issue":"6","key":"4792_CR7","doi-asserted-by":"crossref","DOI":"10.2196\/23105","volume":"7","author":"YA Argyris","year":"2021","unstructured":"Argyris, Y. A., Monu, K., Tan, P.-N., Aarts, C., Jiang, F., & Wiseley, K. A. (2021). Using machine learning to compare provaccine and antivaccine discourse among the public on social media: Algorithm development study. JMIR Public Health and Surveillance, 7(6), e23105.","journal-title":"JMIR Public Health and Surveillance"},{"key":"4792_CR8","doi-asserted-by":"publisher","DOI":"10.1109\/TEM.2021.3101590","author":"S Bag","year":"2021","unstructured":"Bag, S., Gupta, S., Choi, T.-M., & Kumar, A. (2021). Roles of innovation leadership on using big data analytics to establish resilient healthcare supply chains to combat the COVID-19 pandemic: A multimethodological study. IEEE Transactions on Engineering Management. https:\/\/doi.org\/10.1109\/TEM.2021.3101590","journal-title":"IEEE Transactions on Engineering Management"},{"issue":"1","key":"4792_CR9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13584-021-00486-6","volume":"10","author":"S Bar-Lev","year":"2021","unstructured":"Bar-Lev, S., Reichman, S., & Barnett-Itzhaki, Z. (2021). Prediction of vaccine hesitancy based on social media traffic among Israeli parents using machine learning strategies. Israel Journal of Health Policy Research, 10(1), 1\u20138.","journal-title":"Israel Journal of Health Policy Research"},{"key":"4792_CR10","doi-asserted-by":"crossref","DOI":"10.1016\/j.puhip.2020.100031","volume":"1","author":"RS Bhopal","year":"2020","unstructured":"Bhopal, R. S. (2020). COVID-19 zugzwang: Potential public health moves towards population (herd) immunity. Public Health in Practice, 1, 100031.","journal-title":"Public Health in Practice"},{"key":"4792_CR11","doi-asserted-by":"crossref","unstructured":"Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual workshop on Computational learning theory (pp. 144\u2013152).","DOI":"10.1145\/130385.130401"},{"issue":"1","key":"4792_CR12","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5\u201332.","journal-title":"Machine Learning"},{"key":"4792_CR13","doi-asserted-by":"crossref","first-page":"1183","DOI":"10.1038\/s41591-020-1011-4","volume":"26","author":"J Budd","year":"2020","unstructured":"Budd, J., Miller, B. S., Manning, E. M., et al. (2020). Digital technologies in the public-health response to COVID-19. Nature Medicine, 26, 1183\u20131192.","journal-title":"Nature Medicine"},{"key":"4792_CR14","doi-asserted-by":"publisher","DOI":"10.1080\/00207543.2020.1766716","author":"B Chen","year":"2020","unstructured":"Chen, B., Bai, R., Li, J., Liu, Y., Xue, N., & Ren, J. (2020). A multiobjective single bus corridor scheduling using machine learning-based predictive models. International Journal of Production Research. https:\/\/doi.org\/10.1080\/00207543.2020.1766716","journal-title":"International Journal of Production Research"},{"key":"4792_CR99","doi-asserted-by":"crossref","unstructured":"Chevallier, C., Hacquin, A. S., & Mercier, H. (2021). COVID-19 vaccine hesitancy: Shortening the last mile. Trends in Cognitive Sciences, 25(5), 331\u2013333.","DOI":"10.1016\/j.tics.2021.02.002"},{"issue":"9","key":"4792_CR15","doi-asserted-by":"crossref","first-page":"1709","DOI":"10.1111\/poms.12832","volume":"27","author":"MC Cohen","year":"2018","unstructured":"Cohen, M. C. (2018). Big data and service operations. Production and Operations Management, 27(9), 1709\u20131723.","journal-title":"Production and Operations Management"},{"issue":"3","key":"4792_CR16","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273\u2013297.","journal-title":"Machine Learning"},{"issue":"19","key":"4792_CR17","doi-asserted-by":"crossref","first-page":"10438","DOI":"10.3390\/ijerph181910438","volume":"18","author":"L-A Cotfas","year":"2021","unstructured":"Cotfas, L.-A., Delcea, C., & Gherai, R. (2021). COVID-19 vaccine hesitancy in the month following the start of the vaccination process. International Journal of Environmental Research and Public Health, 18(19), 10438.","journal-title":"International Journal of Environmental Research and Public Health"},{"issue":"1","key":"4792_CR18","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"13","author":"T Cover","year":"1967","unstructured":"Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. Information Theory, 13(1), 21\u201327.","journal-title":"Information Theory"},{"issue":"10","key":"4792_CR19","doi-asserted-by":"crossref","first-page":"1749","DOI":"10.1111\/poms.12707","volume":"27","author":"R Cui","year":"2018","unstructured":"Cui, R., Gallino, S., Moreno, A., & Zhang, D. J. (2018). The operational value of social media information. Production and Operations Management, 27(10), 1749\u20131769.","journal-title":"Production and Operations Management"},{"key":"4792_CR20","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4612-0711-5","volume-title":"A probabilistic theory of pattern recognition","author":"L Devroye","year":"1996","unstructured":"Devroye, L., Gy\u00f6rfi, L., & Lugosi, G. (1996). A probabilistic theory of pattern recognition. Springer."},{"issue":"35","key":"4792_CR21","doi-asserted-by":"crossref","first-page":"5273","DOI":"10.1016\/j.vaccine.2018.07.046","volume":"36","author":"GJ Domek","year":"2018","unstructured":"Domek, G. J., O\u2019Leary, S. T., Bull, S., Bronsert, M., Contreras-Roldan, I. L., Ventura, G. A., Kempe, A., & Asturias, E. J. (2018). Measuring vaccine hesitancy: Field testing the WHO SAGE working group on vaccine hesitancy survey tool in Guatemala. Vaccine, 36(35), 5273\u20135281.","journal-title":"Vaccine"},{"key":"4792_CR188","doi-asserted-by":"crossref","unstructured":"Du, J., Cunningham, R. M., Xiang, Y., et al. (2020). Leveraging deep learning to understand health beliefs about the Human Papillomavirus Vaccine from social media. NPJ Digital Medicine, 2\u201327.","DOI":"10.1038\/s41746-019-0102-4"},{"issue":"1","key":"4792_CR22","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1586\/14760584.2015.964212","volume":"14","author":"E Dub\u00e9","year":"2015","unstructured":"Dub\u00e9, E., Vivion, M., & MacDonald, N. E. (2015). Vaccine hesitancy, vaccine refusal and the antivaccine movement: influence, impact and implications. Expert Review of Vaccines, 14(1), 99\u2013117.","journal-title":"Expert Review of Vaccines"},{"key":"4792_CR23","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1146\/annurev-publhealth-090419-102240","volume":"42","author":"\u00c8 Dub\u00e9","year":"2021","unstructured":"Dub\u00e9, \u00c8., Ward, J. K., Verger, P., & MacDonald, N. E. (2021). Vaccine hesitancy, acceptance, and anti-vaccination: trends and future prospects for public health. Annual Review of Public Health, 42, 175\u2013191.","journal-title":"Annual Review of Public Health"},{"issue":"1","key":"4792_CR24","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1007\/s10479-017-2676-z","volume":"283","author":"R Dubey","year":"2019","unstructured":"Dubey, R., Altay, N., & Blome, C. (2019a). Swift trust and commitment: The missing links for humanitarian supply chain coordination? Annals of Operations Research, 283(1), 159\u2013177.","journal-title":"Annals of Operations Research"},{"issue":"1","key":"4792_CR25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10479-019-03440-7","volume":"283","author":"R Dubey","year":"2019","unstructured":"Dubey, R., Gunasekaran, A., & Papadopoulos, T. (2019b). Disaster relief operations: Past, present and future. Annals of Operations Research, 283(1), 1\u20138.","journal-title":"Annals of Operations Research"},{"issue":"1","key":"4792_CR26","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/s10479-017-2452-0","volume":"283","author":"S DuHadway","year":"2019","unstructured":"DuHadway, S., Carnovale, S., & Hazen, B. (2019). Understanding risk management for intentional supply chain disruptions: Risk detection, risk mitigation, and risk recovery. Annals of Operations Research, 283(1), 179\u2013198.","journal-title":"Annals of Operations Research"},{"key":"4792_CR27","doi-asserted-by":"crossref","first-page":"120903","DOI":"10.1016\/j.techfore.2021.120903","volume":"170","author":"P Eachempati","year":"2021","unstructured":"Eachempati, P., Srivastava, P. R., Kumar, A., Tan, K. H., & Gupta, S. (2021). Validating the impact of accounting disclosures on stock market: A deep neural network approach. Technological Forecasting and Social Change, 170, 120903.","journal-title":"Technological Forecasting and Social Change"},{"key":"4792_CR125","unstructured":"ElonPoll. (2020). North Carolina willingness to take COVID-19 vaccine. The Charlotte Observer, The Durham Herald-Sun & The Raleigh News & Observer."},{"issue":"1","key":"4792_CR28","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1007\/s10479-017-2480-9","volume":"263","author":"SM Fast","year":"2018","unstructured":"Fast, S. M., Kim, L., Cohn, E. L., Mekaru, S. R., Brownstein, J. S., & Markuzon, N. (2018). Predicting social response to infectious disease outbreaks from internet-based news streams. Annals of Operations Research, 263(1), 551\u2013564.","journal-title":"Annals of Operations Research"},{"issue":"34","key":"4792_CR29","first-page":"4161","volume":"18","author":"C Fornell","year":"2015","unstructured":"Fornell, C., Larcker, D. F., & MacDonald, N. E. (2015). Vaccine hesitancy: Definition, scope and determinants. Journal of Marketing Research, 18(34), 4161\u20134164.","journal-title":"Journal of Marketing Research"},{"key":"4792_CR121","doi-asserted-by":"crossref","unstructured":"Freeman, D., Waite, F., Rosebrock, L., Petit, A., Causier, C., East, A., & Lambe, S. (2022). Coronavirus conspiracy beliefs, mistrust, and compliance with government guidelines in England. Psychological Medicine, 52(2), 251\u2013263.","DOI":"10.1017\/S0033291720001890"},{"key":"4792_CR30","doi-asserted-by":"crossref","unstructured":"Freund, Y., & Schapire, R. E. (1995). A decision-theoretic generalization of on-line learning and an application to boosting. In European Conference on Computational Learning Theory, pp. 23\u201337.","DOI":"10.1007\/3-540-59119-2_166"},{"key":"4792_CR31","doi-asserted-by":"crossref","DOI":"10.1016\/j.chb.2021.106751","volume":"120","author":"M Furini","year":"2021","unstructured":"Furini, M. (2021). Identifying the features of ProVax and NoVax groups from social media conversations. Computers in Human Behavior, 120, 106751.","journal-title":"Computers in Human Behavior"},{"issue":"3","key":"4792_CR32","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0247642","volume":"16","author":"F Germani","year":"2021","unstructured":"Germani, F., & Biller-Andorno, N. (2021). The anti-vaccination infodemic on social media: A behavioral analysis. PLoS ONE, 16(3), e0247642.","journal-title":"PLoS ONE"},{"key":"4792_CR33","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A., & Hinton, G. (2013). Speech recognition with deep recurrent neural networks. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), no. 3, pp. 6645\u20136649.","DOI":"10.1109\/ICASSP.2013.6638947"},{"issue":"1","key":"4792_CR34","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/s10479-017-2607-z","volume":"283","author":"DA Griffith","year":"2019","unstructured":"Griffith, D. A., Boehmke, B., Bradley, R. V., Hazen, B. T., & Johnson, A. W. (2019). Embedded analytics: Improving decision support for humanitarian logistics operations. Annals of Operations Research, 283(1), 247\u2013265.","journal-title":"Annals of Operations Research"},{"key":"4792_CR35","doi-asserted-by":"crossref","first-page":"2223","DOI":"10.1093\/rfs\/hhaa009","volume":"33","author":"S Gu","year":"2020","unstructured":"Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33, 2223\u20132273.","journal-title":"The Review of Financial Studies"},{"key":"4792_CR36","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-021-03962-z","author":"M Gupta","year":"2021","unstructured":"Gupta, M., Shoja, A., & Mikalef, P. (2021). Toward the understanding of national culture in the success of non-pharmaceutical technological interventions in mitigating COVID-19 pandemic. Annals of Operations Research. https:\/\/doi.org\/10.1007\/s10479-021-03962-z","journal-title":"Annals of Operations Research"},{"issue":"4","key":"4792_CR37","doi-asserted-by":"crossref","first-page":"e26627","DOI":"10.2196\/26627","volume":"23","author":"A Hussain","year":"2021","unstructured":"Hussain, A., Tahir, A., Hussain, Z., Sheikh, Z., Gogate, M., Dashtipour, K., Ali, A., & Sheikh, A. (2021). Artificial intelligence\u2013enabled analysis of public attitudes on facebook and twitter toward covid-19 vaccines in the united kingdom and the united states: Observational study. Journal of Medical Internet Research, 23(4), e26627.","journal-title":"Journal of Medical Internet Research"},{"issue":"10","key":"4792_CR38","doi-asserted-by":"crossref","first-page":"2904","DOI":"10.1080\/00207543.2020.1750727","volume":"58","author":"D Ivanov","year":"2020","unstructured":"Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. International Journal of Production Research, 58(10), 2904\u20132915.","journal-title":"International Journal of Production Research"},{"issue":"9","key":"4792_CR39","first-page":"205630512110484","volume":"23","author":"X Jiang","year":"2021","unstructured":"Jiang, X., Su, M.-H., Hwang, J., Lian, R., Brauer, M., Kim, S., & Chin, J. (2021). Identifying false human papillomavirus (HPV) vaccine information and corresponding risk perceptions from twitter: advanced predictive models. Journal of Medical Internet Research, 23(9), 20563051211048412.","journal-title":"Journal of Medical Internet Research"},{"key":"4792_CR40","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-021-04397-2","author":"K Kapoor","year":"2021","unstructured":"Kapoor, K., Bigdeli, A. Z., Dwivedi, Y. K., & Raman, R. (2021). How is COVID-19 altering the manufacturing landscape? A literature review of imminent challenges and management interventions. Annals of Operations Research. https:\/\/doi.org\/10.1007\/s10479-021-04397-2","journal-title":"Annals of Operations Research"},{"issue":"2","key":"4792_CR41","doi-asserted-by":"crossref","DOI":"10.2196\/17149","volume":"7","author":"E Karafillakis","year":"2021","unstructured":"Karafillakis, E., Martin, S., Simas, C., Olsson, K., Takacs, J., Dada, S., & Larson, H. J. (2021). Methods for social media monitoring related to vaccination: Systematic scoping review. JMIR Public Health and Surveillance, 7(2), e17149.","journal-title":"JMIR Public Health and Surveillance"},{"issue":"10","key":"4792_CR42","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.3390\/vaccines9101059","volume":"9","author":"A Karami","year":"2021","unstructured":"Karami, A., Zhu, M., Goldschmidt, B., Boyajieff, H. R., & Najafabadi, M. M. (2021). COVID-19 vaccine and social media in the US: Exploring emotions and discussions on twitter. Vaccines, 9(10), 1059.","journal-title":"Vaccines"},{"key":"4792_CR43","first-page":"1","volume":"4","author":"A Kumar","year":"2021","unstructured":"Kumar, A., Choi, T. M., Wamba, S. F., Gupta, S., & Tan, K. H. (2021a). Infection vulnerability stratification risk modelling of COVID-19 data: A deterministic SEIR epidemic model analysis. Annals of Operations Research, 4, 1\u201327.","journal-title":"Annals of Operations Research"},{"key":"4792_CR44","doi-asserted-by":"crossref","first-page":"113728","DOI":"10.1016\/j.dss.2021.113728","volume":"155","author":"A Kumar","year":"2022","unstructured":"Kumar, A., Gopal, R. D., Shankar, R., & Tan, K. H. (2022). Fraudulent review detection model focusing on emotional expressions and explicit aspects: Investigating the potential of feature engineering. Decision Support Systems, 155, 113728.","journal-title":"Decision Support Systems"},{"key":"4792_CR45","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1016\/j.indmarman.2019.05.003","volume":"90","author":"A Kumar","year":"2020","unstructured":"Kumar, A., Shankar, R., & Aljohani, N. (2020). A big data driven framework for demand-driven forecasting with effects of marketing-mix variables. Industrial Marketing Management, 90, 493\u2013507.","journal-title":"Industrial Marketing Management"},{"key":"4792_CR46","first-page":"428","volume":"27","author":"A Kumar","year":"2018","unstructured":"Kumar, A., Shankar, R., Choudhary, A., & Thakur, L. (2018). A big data MapReduce framework for fault diagnosis in cloud-based manufacturing. International Journal of Production Research, 27, 428\u2013439.","journal-title":"International Journal of Production Research"},{"key":"4792_CR47","first-page":"7060","volume":"54","author":"A Kumar","year":"2016","unstructured":"Kumar, A., Shankar, R., & Thakur, L. (2016). A big data driven sustainable manufacturing framework for condition-based maintenance prediction. Journal of Computational Science, 54, 7060\u20137073.","journal-title":"Journal of Computational Science"},{"key":"4792_CR48","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-021-03955-y","author":"S Kumar","year":"2021","unstructured":"Kumar, S., Xu, C., Ghildayal, N., Chandra, C., & Yang, M. (2021b). Social media effectiveness as a humanitarian response to mitigate influenza epidemic and COVID-19 pandemic. Annals of Operations Research. https:\/\/doi.org\/10.1007\/s10479-021-03955-y","journal-title":"Annals of Operations Research"},{"issue":"5","key":"4792_CR49","doi-asserted-by":"crossref","first-page":"1594","DOI":"10.1080\/00207543.2019.1662133","volume":"58","author":"A Kusiak","year":"2020","unstructured":"Kusiak, A. (2020). Convolutional and generative adversarial neural networks in manufacturing. International Journal of Production Research, 58(5), 1594\u20131604.","journal-title":"International Journal of Production Research"},{"key":"4792_CR50","doi-asserted-by":"publisher","DOI":"10.2196\/26953","author":"SWH Kwok","year":"2021","unstructured":"Kwok, S. W. H., Vadde, S. K., & Wang, G. (2021). Twitter speaks: An analysis of Australian twitter users\u2019 topics and sentiments about COVID-19 vaccination using machine learning. Journal of Medical Internet Research. https:\/\/doi.org\/10.2196\/26953","journal-title":"Journal of Medical Internet Research"},{"key":"4792_CR180","doi-asserted-by":"crossref","unstructured":"Lazarus, J. V., Ratzan, S. C., Palayew, A. Gostin, L. O., Larson, H. J., Rabin, K., Kimball, S., & El-Mohandes, A. (2021). A global survey of potential acceptance of a Covid-19 vaccine. Nature Medicine, 27, 225\u2013228.","DOI":"10.1038\/s41591-020-1124-9"},{"key":"4792_CR51","unstructured":"Ma, P., Zeng-Treitler, Q., & Nelson, S. J. (2021). Use of two topic modeling methods to investigate covid vaccine hesitancy. International Conference on ICT, Society and Human Beings, 384, 221\u2013226."},{"key":"4792_CR52","doi-asserted-by":"crossref","unstructured":"Majumdar, P., Biswas, A., & Sahu S. (2020). COVID-19 pandemic and lockdown: Cause of sleep disruption, depression, somatic pain, and increased screen exposure of office workers and students of India. Chronobiology International, 37(8), 1191\u20131200.","DOI":"10.1080\/07420528.2020.1786107"},{"key":"4792_CR175","doi-asserted-by":"crossref","unstructured":"Martinez-Rojas, M., del Carmen Pardo-Ferreira, M., & Rubio-Romero, J. C. (2018). Twitter as a tool for the management and analysis of emergency situations: A systematic literature review. International Journal of Information Management, 43, 196\u2013208.","DOI":"10.1016\/j.ijinfomgt.2018.07.008"},{"issue":"1","key":"4792_CR53","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1007\/s10479-016-2303-4","volume":"270","author":"N Mishra","year":"2018","unstructured":"Mishra, N., & Singh, A. (2018). Use of twitter data for waste minimisation in beef supply chain. Annals of Operations Research, 270(1), 337\u2013359.","journal-title":"Annals of Operations Research"},{"issue":"4","key":"4792_CR54","doi-asserted-by":"crossref","DOI":"10.2196\/jmir.1933","volume":"15","author":"SA Moorhead","year":"2013","unstructured":"Moorhead, S. A., Hazlett, D. E., Harrison, L., Carroll, J. K., Irwin, A., & Hoving, C. (2013). A new dimension of health care: Systematic review of the uses, benefits, and limitations of social media for health communication. Journal of Medical Internet Research, 15(4), e1933.","journal-title":"Journal of Medical Internet Research"},{"key":"4792_CR199","doi-asserted-by":"crossref","unstructured":"Narazaki, H., & Shigaki, I. (1999). A machine-learning approach for a sintering process using a neural network. Production Planning and Control, 10(8), 727\u2013734.","DOI":"10.1080\/095372899232551"},{"key":"4792_CR55","doi-asserted-by":"crossref","first-page":"1126","DOI":"10.3390\/vaccines9101126","volume":"9","author":"A Odone","year":"2021","unstructured":"Odone, A., Gianfredi, V., Sorbello, S., Capraro, M., Frascella, B., Vigezzi, G. P., & Signorelli, C. (2021). The use of digital technologies to support vaccination programmes in Europe: State of the art and best practices from experts\u2019 interviews. Vaccines, 9, 1126.","journal-title":"Vaccines"},{"key":"4792_CR140","doi-asserted-by":"crossref","unstructured":"Palamenghi, L., Barello, S., Boccia, S., & Graffigna, G. (2020). Mistrust in biomedical research and vaccine hesitancy: The forefront challenge in the battle against Covid-19 in Italy. European Journal of Epidemiology, 35(8), 785\u2013788.","DOI":"10.1007\/s10654-020-00675-8"},{"issue":"1","key":"4792_CR56","doi-asserted-by":"crossref","first-page":"28","DOI":"10.3390\/vaccines9010028","volume":"9","author":"H Piedrahita-Vald\u00e9s","year":"2021","unstructured":"Piedrahita-Vald\u00e9s, H., Piedrahita-Castillo, D., Bermejo-Higuera, J., Guillem-Saiz, P., Bermejo-Higuera, J. R., Guillem-Saiz, J., Sicilia-Montalvo, J. A., & Mach\u00edo-Regidor, F. (2021). Vaccine hesitancy on social media: Sentiment analysis from June 2011 to April 2019. Vaccines, 9(1), 28.","journal-title":"Vaccines"},{"key":"4792_CR57","doi-asserted-by":"crossref","unstructured":"Pujawan, I. N., & Bah, A. U. (2021). Supply chains under COVID-19 disruptions: literature review and research agenda. In Supply Chain Forum: An International Journal, pp. 1\u201315. Taylor & Francis.","DOI":"10.1080\/16258312.2021.1932568"},{"key":"4792_CR58","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-021-04154-5","author":"A Qayyum","year":"2021","unstructured":"Qayyum, A., Razzak, I., Tanveer, M., & Kumar, A. (2021). Depth-wise dense neural network for automatic COVID19 infection detection and diagnosis. Annals of operations research. https:\/\/doi.org\/10.1007\/s10479-021-04154-5","journal-title":"Annals of operations research"},{"key":"4792_CR59","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-020-03685-7","author":"MM Queiroz","year":"2020","unstructured":"Queiroz, M. M., Ivanov, D., Dolgui, A., & Wamba, S. F. (2020). Impacts of epidemic outbreaks on supply chains: Mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Annals of Operations Research. https:\/\/doi.org\/10.1007\/s10479-020-03685-7","journal-title":"Annals of Operations Research"},{"key":"4792_CR150","doi-asserted-by":"crossref","unstructured":"Quyen, T., To, K. G., Huynh, V. -A. N., Nguyen, N. T. Q., Ngo, D. T. N., Alley, S. J., Tran, A. N. Q., Tran, A. N. P., Pham, N. T. T., Bui, T. X., et al. (2021). Applying machine learning to identify anti-vaccination tweets during the COVID-19 pandemic. International Journal of Environmental Research and Public Health, 18, 4069.","DOI":"10.3390\/ijerph18084069"},{"key":"4792_CR123","doi-asserted-by":"crossref","unstructured":"Reinhardt, A., & Rossmann, C. (2021). Age-related framing effects: Why vaccination against COVID-19 should be promoted differently in younger and older adults. Journal of Experimental Psychology: Applied, 27(4), 669\u2013678.","DOI":"10.1037\/xap0000378"},{"key":"4792_CR100","unstructured":"Researcher, C. Q. (2020). Issues for debate in american public policy: Selections from CQ researcher (pp. 22). SAGE Publications."},{"issue":"24","key":"4792_CR60","doi-asserted-by":"crossref","first-page":"9019","DOI":"10.3390\/app10249019","volume":"10","author":"A Rodr\u00edguez-Gonz\u00e1lez","year":"2020","unstructured":"Rodr\u00edguez-Gonz\u00e1lez, A., Tu\u00f1as, J. M., Prieto Santamar\u00eda, L., Fern\u00e1ndezPeces-Barba, D., Menasalvas Ruiz, E., Jaramillo, A., Cotarelo, M., ConejoFern\u00e1ndez, A. J., Arce, A., & Gil, A. (2020). Identifying polarity in tweets from an imbalanced dataset about diseases and vaccines using a meta-model based on machine learning techniques. Applied Sciences, 10(24), 9019.","journal-title":"Applied Sciences"},{"key":"4792_CR61","doi-asserted-by":"crossref","first-page":"91886","DOI":"10.1109\/ACCESS.2020.2993967","volume":"8","author":"RF Sear","year":"2020","unstructured":"Sear, R. F., Vel\u00e1squez, N., Leahy, R., Restrepo, N. J., El Oud, S., Gabriel, N., Lupu, Y., & Johnson, N. F. (2020). Quantifying COVID-19 content in the online health opinion war using machine learning. IEEE Access, 8, 91886\u201391893.","journal-title":"IEEE Access"},{"key":"4792_CR62","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9781107298019","volume-title":"Understanding machine learning: From theory to algorithms","author":"S Shalev-Shwartz","year":"2014","unstructured":"Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding machine learning: From theory to algorithms. Cambridge University Press."},{"issue":"1","key":"4792_CR64","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1007\/s10479-017-2522-3","volume":"283","author":"JP Singh","year":"2019","unstructured":"Singh, J. P., Dwivedi, Y. K., Rana, N. P., Kumar, A., & Kapoor, K. K. (2019). Event classification and location prediction from tweets during disasters. Annals of Operations Research, 283(1), 737\u2013757.","journal-title":"Annals of Operations Research"},{"key":"4792_CR130","unstructured":"Skinner, G. (2020). Who\u2019s least likely to say they\u2019ll get a Covid-19 vaccine? Ipsos Mori. https:\/\/www.ipsos.com\/ipsosmori\/en-uk\/whos-least-likely-say-theyll-get-covid-19-vaccine."},{"issue":"5","key":"4792_CR65","doi-asserted-by":"crossref","first-page":"1062","DOI":"10.1080\/21645515.2020.1714311","volume":"16","author":"L Tavoschi","year":"2020","unstructured":"Tavoschi, L., Quattrone, F., D\u2019Andrea, E., Ducange, P., Vabanesi, M., Marcelloni, F., & Lopalco, P. L. (2020). Twitter as a sentinel tool to monitor public opinion on vaccination: An opinion mining analysis from September 2016 to August 2017 in Italy. Human Vaccines & Immunotherapeutics, 16(5), 1062\u20131069.","journal-title":"Human Vaccines & Immunotherapeutics"},{"key":"4792_CR144","doi-asserted-by":"crossref","unstructured":"Tchuente, D., & Nyawa, S. (2021). Real estate price estimation in French cities using geocoding and machine learning. Annals of Operations Research, 308(1), 571\u2013608.","DOI":"10.1007\/s10479-021-03932-5"},{"key":"4792_CR101","doi-asserted-by":"crossref","unstructured":"Thelwall, M., Kousha, K., & Thelwall, S. (2021). Covid-19 vaccine hesitancy on English-language Twitter. Profesional de la informaci\u00f3n, 30(2), e300212.","DOI":"10.3145\/epi.2021.mar.12"},{"issue":"8","key":"4792_CR66","doi-asserted-by":"crossref","first-page":"4069","DOI":"10.3390\/ijerph18084069","volume":"18","author":"QG To","year":"2021","unstructured":"To, Q. G., To, K. G., Huynh, V. A., Nguyen, N. T., Ngo, D. T., Alley, S. J., Tran, A. N., Tran, A. N., Pham, N. T., Bui, T. X., & Vandelanotte, C. (2021). Applying machine learning to identify anti-vaccination tweets during the COVID-19 pandemic. International Journal of Environmental Research and Public Health, 18(8), 4069.","journal-title":"International Journal of Environmental Research and Public Health"},{"issue":"9","key":"4792_CR67","doi-asserted-by":"crossref","DOI":"10.2196\/30451","volume":"23","author":"T Tomaszewski","year":"2021","unstructured":"Tomaszewski, T., Morales, A., Lourentzou, I., Caskey, R., Liu, B., Schwartz, A., & Chin, J. (2021). Identifying false human papillomavirus (HPV) vaccine information and corresponding risk perceptions from twitter: Advanced predictive models. Journal of Medical Internet Research, 23(9), e30451.","journal-title":"Journal of Medical Internet Research"},{"issue":"15","key":"4792_CR68","doi-asserted-by":"crossref","first-page":"2079","DOI":"10.1016\/j.vaccine.2019.02.056","volume":"37","author":"F Verelst","year":"2019","unstructured":"Verelst, F., Kessels, R., Delva, W., Beutels, P., & Willem, L. (2019). Drivers of vaccine decision-making in South Africa: A discrete choice experiment. Vaccine, 37(15), 2079\u20132089.","journal-title":"Vaccine"},{"key":"4792_CR200","doi-asserted-by":"crossref","unstructured":"Wagner, A. L., Huang, Z., Ren, J., Laffoon, M., Ji, M., Pinckney, L. C., Sun, X., Prosser, L. A., Boulton, M. L., & Zikmund-Fisher, B. J. (2020). Vaccine hesitancy and concerns about vaccine safety and effectiveness in Shanghai, China. American Journal of Preventive Medicine, 60(1), S77\u2013S86.","DOI":"10.1016\/j.amepre.2020.09.003"},{"issue":"1","key":"4792_CR69","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1007\/s10479-017-2545-9","volume":"283","author":"SF Wamba","year":"2019","unstructured":"Wamba, S. F., Edwards, A., & Akter, S. (2019). Social media adoption and use for improved emergency services operations: The case of the NSW SES. Annals of Operations Research, 283(1), 225\u2013245.","journal-title":"Annals of Operations Research"},{"issue":"6","key":"4792_CR70","doi-asserted-by":"crossref","first-page":"2193","DOI":"10.1109\/JBHI.2020.3037027","volume":"25","author":"Z Wang","year":"2020","unstructured":"Wang, Z., Yin, Z., & Argyris, Y. A. (2020). Detecting medical misinformation on social media using multimodal deep learning. IEEE Journal of Biomedical and Health Informatics, 25(6), 2193\u20132203.","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"issue":"9","key":"4792_CR71","doi-asserted-by":"crossref","first-page":"1495","DOI":"10.1108\/IJOPM-12-2020-0901","volume":"41","author":"Y Xiong","year":"2021","unstructured":"Xiong, Y., Lam, H. K. S., Kumar, A., Ngai, E. W. T., Xiu, C., & Wang, X. (2021). The mitigating role of blockchain-enabled supply chains during the COVID-19 pandemic. International Journal of Operations & Production Management, 41(9), 1495\u20131521.","journal-title":"International Journal of Operations & Production Management"},{"issue":"3","key":"4792_CR72","doi-asserted-by":"crossref","first-page":"205630511986546","DOI":"10.1177\/2056305119865465","volume":"5","author":"X Yuan","year":"2019","unstructured":"Yuan, X., Schuchard, R. J., & Crooks, A. T. (2019). Examining emergent communities and social bots within the polarized online vaccination debate in Twitter. Social Media + Society, 5(3), 205630511986546.","journal-title":"Social Media + Society"},{"key":"4792_CR185","doi-asserted-by":"crossref","unstructured":"Zhang, S., Zhang, C., & Yang, Q. (2003). Data preparation for data mining. Applied Artificial Intelligence, 17, 375\u2013381.","DOI":"10.1080\/713827180"},{"key":"4792_CR133","doi-asserted-by":"crossref","unstructured":"Zhang, L., Fan, H., Peng, C., Rao, G., & Cong, Q. (2020). Sentiment analysis methods for HPV vaccines related tweets based on transfer learning. Healthcare, 8, 307.","DOI":"10.3390\/healthcare8030307"},{"key":"4792_CR73","unstructured":"Zhou, X., Coiera, E., Tsafnat, G., Arachi, D., Ong, M.-S., & Dunn, A. G. (2015). Using social connection information to improve opinion mining: Identifying negative sentiment about HPV vaccines on Twitter."}],"container-title":["Annals of Operations Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10479-022-04792-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10479-022-04792-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10479-022-04792-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T18:24:33Z","timestamp":1722363873000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10479-022-04792-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,16]]},"references-count":89,"journal-issue":{"issue":"1-2","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["4792"],"URL":"https:\/\/doi.org\/10.1007\/s10479-022-04792-3","relation":{},"ISSN":["0254-5330","1572-9338"],"issn-type":[{"value":"0254-5330","type":"print"},{"value":"1572-9338","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,16]]},"assertion":[{"value":"17 May 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}