Abstract
COVID-19 has been spreading across the world starting from early 2020, and there are numerous and varied discussions on social media platforms related to the COVID-19 outbreak. In this study, we analyzed and compared the data on Twitter and Weibo at different times based on the public’s understanding of COVID-19 to ultimately understand the characteristics of social media reaction in U.S. and in China during the pandemic. Results show that both similarities and differences existed when comparing the public reaction on social media in the U.S. and in China. The study suggests that data from social media could be used as a good reflection of the public’s reaction, especially in a pandemic like COVID-19. It is important for the government to understand people’s timely reaction during the pandemic in order to ensure the authorities are on the right direction to provide services and accurate information to the public.
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References
Agrawal, A.J.: It’s Not All Bad: The Social Good of Social Media. Forbes, March 2016. https://www.forbes.com/sites/ajagrawal/2016/03/18/its-not-all-bad-the-social-good-of-social-media/#6db6c224756f
Lin, C.Y.: Social reaction toward the 2019 novel coronavirus (COVID-19). Soc. Health Behav. 3(1), 1 (2020)
Chang, J., Rosenn, I., Backstrom, L., Marlow, C.: epluribus: ethnicity on social networks. In: Fourth International AAAI Conference on Weblogs and Social Media, pp. 18–25, May 2010
Bessi, A., Coletto, M., Davidescu, G.A., Scala, A., Caldarelli, G., Quattrociocchi, W.: Science vs conspiracy: collective narratives in the age of misinformation. PLoS ONE 10(2), e0118093 (2015)
Pennacchiotti, M., Popescu, A.M.: A machine learning approach to twitter user classification. In: Fifth International AAAI Conference on Weblogs and Social Media, vol. 5, no. 1, July 2011
Ribeiro, M.H., Calais, P.H., Santos, Y.A., Almeida, V.A., Meira Jr, W.: Characterizing and detecting hateful users on twitter. In: Twelfth International AAAI Conference on Web and Social Media, vol. 12, no. 1, June 2018
Badawy, A., Ferrara, E., Lerman, K.: Analyzing the digital traces of political manipulation: the 2016 Russian interference twitter campaign. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 258–265. IEEE, August 2018
Butts, C.T.: Tools for social network analysis. R package version, 2 (2010)
Kim, J., Hastak, M.: Social network analysis: the role of social media after a disaster. In: 10th Anniversary Homeland Defense/Security Education Summit, vol. 38, no. 1, pp. 86–96 (2017)
Houston, J.B., et al.: Social media and disasters: a functional framework for social media use in disaster planning, response, and research. Disasters 39(1), 1–22 (2015)
Gao, H., Barbier, G., Goolsby, R.: Harnessing the crowdsourcing power of social media for disaster relief. IEEE Intell. Syst. 26(3), 10–14 (2011)
Lyu, H., Chen, L., Wang, Y., Luo, J.: Sense and Sensibility: Characterizing Social Media Users Regarding the Use of Controversial Terms for COVID-19. arXiv preprint arXiv:2004.06307 (2020)
Cinelli, M., et al.: The covid-19 social media infodemic. arXiv preprint arXiv:2003.05004 (2020)
Li, C., Chen, L.J., Chen, X., Zhang, M., Pang, C.P., Chen, H.: Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020. Eurosurveillance 25(10), 2000199 (2020)
Depoux, A., Martin, S., Karafillakis, E., Preet, R., Wilder-Smith, A., Larson, H.: The pandemic of social media panic travels faster than the COVID-19 outbreak. J. Travel Med. 27(3) (2020)
Mejía, F., Brooks, I., Marti, M., David, N.O., Cosio, G.D.: Fear on the networks: analyzing the 2014 Ebola outbreak. Revista Panamericana de Salud Pública 41, e134 (2018)
Wojcik, S., Hughes, A.: Sizing Up Twitter Users. Pew Research Center (2019). https://www.pewresearch.org/internet/2019/04/24/sizing-up-twitter-users/
Li, L., et al.: Characterizing the propagation of situational information in social media during covid-19 epidemic: a case study on weibo. IEEE Trans. Comput. Soc. Syst. 7(2), 556–562 (2020)
Zhao, Y., Cheng, S., Yu, X., Xu, H.: Chinese public’s attention to the COVID-19 epidemic on social media: observational descriptive study. J. Med. Internet Res. 22(5), e18825 (2020)
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Wang, T., Brooks, I., Bashir, M. (2022). Public Reaction on Social Media During COVID-19: A Comparison Between Twitter and Weibo. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 283. Springer, Cham. https://doi.org/10.1007/978-3-030-80119-9_38
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DOI: https://doi.org/10.1007/978-3-030-80119-9_38
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