A journal of IEEE and CAA , publishes
high-quality papers in English on original
theoretical/experimental research
and development in all areas of automation
Volume 10
Issue 4
IEEE/CAA Journal of Automatica Sinica
| Citation: | H. Y. Liu, M. C. Zhou, X. Y. Lu, A. Abusorrah, and Y. Al-Turki, “Analysis of evolutionary social media activities: Pre-vaccine and post-vaccine emergency use,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 1090–1092, Apr. 2023. doi: 10.1109/JAS.2023.123156
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M. A. Ferrag, et al., “Fighting COVID-19 and future pandemics with the internet of things: Security and privacy perspectives,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 9, pp. 1477–1499, Sept. 2021. doi: 10.1109/JAS.2021.1004087
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E. F. Ohata, et al., “Automatic detection of COVID-19 infection using chest X-ray images through transfer learning,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 1, pp. 239–248, Jan. 2021.
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S. Gottlieb, “America needs to win the coronavirus vaccine race,” Wall Street J.. Apr. 26, 2020. [Online], Available: https://www.wsj.com/articles/america-needs-to-win-the-coronavirus-vaccine-race-11587924258, Accessed Feb. 4, 2021.
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M. DeVerna, et al., “CoVaxxy: A global collection of English Twitter posts about COVID-19 vaccines,” arXiv preprints: arXiv-2101, 2021.
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R. Gallotti, F. Valle, N., P. Sacco, and D. Manlio, “Assessing the risks of ‘infodemics’ in response to COVID-19 epidemics,” Nature Human Behaviour, vol. 4, no. 12, pp. 1285–1293, 2020. doi: 10.1038/s41562-020-00994-6
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W. Jennings, G. Stoker, H. Bunting, V. Valgarðsson, J. Gaskell, D. Devine, L. McKay, and M. C. Mills, “Lack of trust, conspiracy beliefs, and social media use predict COVID-19 vaccine hesitancy,” Vaccines, vol. 9, no. 6, p. 593, 2021. doi: 10.3390/vaccines9060593
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K. Sharma, et al., “COVID-19 vaccines: Characterizing misinformation campaigns and vaccine hesitancy on Twitter,” arXiv preprint arXiv: 2106.08423, 2021.
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K. Garcia and L. Berton, “Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA.” Applied Soft Computing, vol. 101, p. 107057, 2021.
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J. Lyu, et al., “COVID-19 vaccine–related discussion on Twitter: Topic modeling and sentiment analysis,” J. Medical Internet Research, vol. 23, no. 6, 2021. doi: 10.2196/24435
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W. Wu, et al., “Characterizing discourse about COVID-19 vaccines: A reddit version of the pandemic story,” arXiv preprint arXiv: 2101.06321, 2021.
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J. M. Banda, et al., “A large-scale COVID-19 Twitter chatter dataset for open scientific research–An international collaboration,” Epidemiologia, vol. 2, no. 3, pp. 315–324, 2021. doi: 10.3390/epidemiologia2030024
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H. Liu, “Analyzing fluctuation of topics and public sentiment through social media data,” Thesis, Newark College of Engineering, New Jersey Institute of Technology, USA, 2022.
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S. G. Bird and L. Edward. “NLTK: The natural language toolkit.” in Proc. COLING/ACL Interactive Presentation Sessions: Association Comput. Linguistics, 2006, pp. 69–72.
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C. J Hutto and E. E. Gilbert, “VADER: A parsimonious rule-based model for sentiment analysis of social media text,” in Proc. Eighth Int. Conf. Weblogs and Social Media, Ann Arbor, USA, June 2014.
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H. Jelodar, et al., “Latent dirichlet allocation (LDA) and topic Modeling: Models, applications, A survey,” Multimedia Tools and Applications 78, vol. 78, no. 11, pp. 15169–15211, 2019. doi: 10.1007/s11042-018-6894-4
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R. Rehurek and P. Sojka, “Gensim–statistical semantics in python,” 2011. [Online], Available: https://www.fi.muni.cz/usr/sojka/posters/rehurek-sojka-scipy2011.pdf.
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H. Liu, et al., “Aspect-based sentiment analysis: A survey of deep learning methods,” IEEE Trans. Computational Social Syst,, vol. 7, no. 6, pp. 1358–1375, Dec. 2020.
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