Perception of COVID-19 vaccination among Indian Twitter users: computational approach
Tóm tắt
Tài liệu tham khảo
World Health Organization. (2021). WHO Coronavirus Disease (COVID-19) Dashboard With Vaccination Data. World Health Organization. https://covid19.who.int./region/searo/country/in. (Accessed 1 Jul 2021).
Sinnenberg, L., Buttenheim, A. M., Padrez, K., et al. (2017). Twitter as a tool for health research: a systematic review. American Journal of Public Health, 107, e1-8. https://doi.org/10.2105/AJPH.2016.303512
Shoaei, M. D., & Dastani, M. (2020). The role of twitter during the COVID-19 crisis: a systematic literature review. Acta Informatica Pragensia, 9, 154–169. https://doi.org/10.18267/J.AIP.138
Charquero-Ballester, M., Walter, J. G., Nissen, I. A., et al. (2021). Different types of COVID-19 misinformation have different emotional valence on Twitter. Big Data & Society. https://doi.org/10.1177/20539517211041279/ASSET/IMAGES/LARGE/10.1177_20539517211041279-FIG2.JPEG
Su, Y., Wu, P., Li, S., et al. (2021). Public emotion responses during COVID-19 in China on social media: an observational study. Human Behavior and Emerging Technologies, 3, 127–136. https://doi.org/10.1002/HBE2.239
Frederiksen, L. S. F., Zhang, Y., Foged, C., et al. (2020). The long road toward COVID-19 herd immunity: Vaccine platform technologies and mass immunization strategies. Frontiers in Immunology, 11, 1817. https://doi.org/10.3389/FIMMU.2020.01817/BIBTEX
Aguas, R., Corder, R.M., & King, J.G., et al. (2022). Herd immunity thresholds for SARS-CoV-2 estimated from unfolding epidemics. medRxiv. https://doi.org/10.1101/2020.07.23.20160762
Sallam, M. (2021). COVID-19 vaccine hesitancy worldwide: a concise systematic review of vaccine acceptance rates. Vaccines, 9, 160. https://doi.org/10.3390/VACCINES9020160
Shaaban, R., Ghazy, R. M., Elsherif, F., et al. (2022). COVID-19 vaccine acceptance among social media users: a content analysis, multi-continent study. International Journal of Environmental Research and Public Health, 19, 5737. https://doi.org/10.3390/IJERPH19095737
Twitter. (2022). About Twitter|Our Company and Priorities. https://about.twitter.com/en (Accessed 23 Nov 2022).
Li, I., Li, Y., Li, T., et al. (2020). What are we depressed about when we talk about COVID-19: Mental health analysis on tweets using natural language processing. Lect Notes Artif Intell Lect Notes Bioinformatics, 12498, 358–370. https://doi.org/10.1007/978-3-030-63799-6_27/COVER
Guntuku, S. C., Sherman, G., Stokes, D. C., et al. (2020). Tracking mental health and symptom mentions on Twitter during COVID-19. Journal of General Internal Medicine, 35, 2798–2800. https://doi.org/10.1007/S11606-020-05988-8/FIGURES/2
Bonnevie, E., Gallegos-Jeffrey, A., Goldbarg, J., et al. (2021). Quantifying the rise of vaccine opposition on Twitter during the COVID-19 pandemic. Journal of Communication in Healthcare, 14, 12–19. https://doi.org/10.1080/17538068.2020.1858222/SUPPL_FILE/YCIH_A_1858222_SM6843.DOCX
Piltch-Loeb, R., & Diclemente, R. (2020). The vaccine uptake continuum: Applying social science theory to shift vaccine hesitancy. Vaccines, 8, 76. https://doi.org/10.3390/VACCINES8010076
Odlum, M., Cho, H., & Broadwell, P., et al. (2020). Application of topic modeling to tweetsas the foundation for health disparity research for COVID-19. In Studies in Health Technology and Informatics. IOS Press, 24–7. https://doi.org/10.3233/SHTI200484
Albalawi, R., Yeap, T. H., & Benyoucef, M. (2020). Using topic modeling methods for short-text data: a comparative analysis. Frontiers in Artificial Intelligence, 3, 42. https://doi.org/10.3389/FRAI.2020.00042/BIBTEX
Xue, J., Chen, J., Hu, R., et al. (2020). Twitter discussions and concerns about COVID-19 pandemic: Twitter data analysis using a machine learning approach. Published Online First. https://doi.org/10.2196/preprints.20550
Nikita, M., & Chaney, N. (2020). Tuning of the latent dirichlet allocation models parameters [R package ldatuning version 1.0.2]. The Comprehensive R Archive Network. https://cran.r-project.org/package=ldatuning (Accessed 28 Jul 2021).
Mohammad, S.M. (2020). Considerations in the effective use of emotion and sentiment lexicons. http://arxiv.org/abs/2011.03492 (Accessed 27 Nov 2022).
Tabak, F.S., & Evrim, V. (2016). Comparison of emotion lexicons. In: 13th HONET-ICT International Symposium on Smart MicroGrids for Sustainable Energy Sources Enabled by Photonics and IoT Sensors, HONET-ICT 2016, pp 154–8. https://doi.org/10.1109/HONET.2016.7753440
Kušen, E., Cascavilla, G., & Figl, K., et al. (2017). Identifying emotions in social media: Comparison of word-emotion lexicons. In: Proceedings - 2017 5th International Conference on Future Internet of Things and Cloud Workshops, W-FiCloud 2017. Institute of Electrical and Electronics Engineers Inc. pp. 132–7. https://doi.org/10.1109/FiCloudW.2017.75
Bravo-Marquez, F., Mendoza, M., & Poblete, B. (2013). Combining strengths, emotions and polarities for boosting Twitter sentiment analysis. In: Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining - WISDOM ’13. pp. 1–9. https://doi.org/10.1145/2502069.2502071
Kaila, R. P., & Prasad, A. V. K. (2020). Informational flow on twitter-corona virus outbreak-topic modelling approach. International Journal Of Advance Research In Engineering, 11, 128–134. https://doi.org/10.34218/IJARET.11.3.2020.011
Hanley, H. (1986). Modern Epidemiology. Boston: Little, Brown & Co. https://www.scirp.org/(S(i43dyn45teexjx455qlt3d2q))/reference/ReferencesPapers.aspx?ReferenceID=1390499 (Accessed 21 Jan 2023).
Sahai, H., & Khurshid, A. (1996). Statistics in epidemiology: Methods, techniques, and applications. Boca Raton: CRC Press Inc.
Hu, T., Wang, S., & Luo, W., et al. (2021). Revealing public opinion towards COVID-19 vaccines with twitter data in the United States: Spatiotemporal perspective. Journal of Medical Internet Research 23(9), e30854. https://doi.org/10.2196/30854.https://www.jmir.org/2021/9/e30854
Madanian, S., Parry, D. T., Airehrour, D., et al. (2019). MHealth and big-data integration: Promises for healthcare system in India. BMJ Health and Care Informatics., 26, 100071. https://doi.org/10.1136/bmjhci-2019-100071
