Emotional Governance in Online Public Opinion during Major Public Health Emergencies: A Case of COVID-19

Xinyu Gao; Mengdi Zhu; Liu Ying

doi:10.54691/c25cdc86

Authors

Xinyu Gao
Mengdi Zhu
Liu Ying

DOI:

https://doi.org/10.54691/c25cdc86

Keywords:

COVID-19; Online Public Opinion; Sentiment Analysis.

Abstract

The coronavirus disease 2019 pandemic, initially identified as the ‘COVID-19’ in China at the end of 2019 has posed significant global public health challenges, highlighting the need for effective online public opinion during crises. This study investigates the evolution of public sentiments on social media during the pandemic, applying text mining and using data collected from Chinese microblogging platforms related to the outbreak based on Python. Sentiment data were categorised and analysed across five major phases of the outbreak, capturing the frequency and types of emotional expressions to explore public opinion dynamics. Results reveal distinct patterns: negative emotions, such as panic and anxiety, were prevalent during the initial outbreak; positive emotions increased during containment phases; and mixed emotions emerged during policy adjustments and case surges. Positive sentiment peaked as pandemic control efforts succeeded and normalcy resumed. The findings further indicate that uncertainty and heightened negative emotions can drive surges in public opinion, while timely, reasonable, and effective government responses can alleviate public anxiety and reduce the risk of secondary crises. These insights highlight the critical role of sentiment analysis in public health communication and crisis management, offering actionable recommendations for mitigating public sentiment risks during future pandemics.

Downloads

Download data is not yet available.

References

[1] McCraty, R., Atkinson, M., Tomasino, D., et al. (1999). The impact of an emotional self-management skills course on psychosocial functioning and autonomic recovery to stress in middle school children. Integrative Physiological and Behavioral Science, 34, 246-268.

[2] Gardner, L., & Stough, C. (2002). Examining the relationship between leadership and emotional intelligence in senior level managers. Leadership & Organization Development Journal, 23(2), 68-78.

[3] Lerner, J. S., Gonzalez, R. M., Small, D. A., et al. (2003). Effects of fear and anger on perceived risks of terrorism: A national field experiment. Psychological Science, 14(2), 144-150.

[4] Rime, B., Mesquita, B., Boca, S., et al. (1991). Beyond the emotional event: Six studies on the social sharing of emotion. Cognition & Emotion, 5(5-6), 435-465.

[5] Mäntylä, M. V., Graziotin, D., & Kuutila, M. (2018). The evolution of sentiment analysis-A review of research topics, venues, and top cited papers. Computer Science Review, 27, 16-32.

[6] Li, M., & An, C. (2008). A Web mining based measurement and monitoring model of urban mass panic in emergency management. In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery (Vol. 4, pp. 366-370). IEEE.

[7] Lee, J.-S., & Adina, N. (2018). Refugee or migrant crisis? Labels, perceived agency, and sentiment polarity in online discussions. Social Media + Society, 4(3).

[8] Hidalgo, C. T., Tan, E. S. H., & Verlegh, P. W. J. (2015). The social sharing of emotion (SSE) in online social networks: A case study in Live Journal. Computers in Human Behavior, 52, 364-372.

[9] Shi, W., Xue, G. C., & He, S. Y. (2022). A review of online public opinion research from an emotional perspective. Library and Intelligence Knowledge, 39(1), 105-118. (In Chinese)

[10] Xu, G., Yu, Z., Yao, H., et al. (2019). Chinese text sentiment analysis based on extended sentiment dictionary. IEEE Access, 7, 43749-43762.

[11] Kaur, S., Geeta, S., & Lalit, K. A. (2018). Sentiment analysis approach based on N-gram and KNN classifier. In 2018 First International Conference on Secure Cyber Computing and Communication (pp. 1-4). IEEE.

[12] Li, S., Zixuan, L., & Yanling, L. (2020). Temporal and spatial evolution of online public sentiment on emergencies. Information Processing & Management, 57(2), 102177.

[13] Wu, P., Liu, H. W., & Shen, S. (2017). Research on emotion recognition of online public opinion based on deep learning and OCC emotion rules. Journal of Intelligence, 36(9), 972-980. (In Chinese)

[14] Zhou, H., Zhang, P. Y., Huang, X. Y., et al. (2024). A study of online public opinion situational awareness from the event system perspective. Journal of Intelligence, 43(2), 135-142, 117. (In Chinese)

[15] Li, C. M., & Xu, S. Q. (2022). A study on the evolution and governance of online public opinion from the perspective of actor network theory. Journal of Intelligence, 41(2), 134-139, 197. (In Chinese)

[16] Zhang, S., & Zhou, Y. (2021). Research on the dissemination mechanism of online public opinion on public health emergencies. Medicine and Society, 34(6), 113-118, 129. (In Chinese)

[17] Gao, G., et al. (2019). A systems dynamics simulation study of network public opinion evolution mechanism. Journal of Global Information Management, 27(4), 189-207.

[18] Hu, L., & Dong, J. (2016). Simulation of strategic behaviour of participating subjects in the process of online public opinion evolution and government guidance. China Soft Science, 10, 50-61. (In Chinese)

[19] Jiang, J. G., & Yan, S. Q. (2018). A study on the evolution of microblog public opinion based on the interaction of theme and emotion: The case of "Red, Yellow and Blue Child Abuse Incident". Journal of Intelligence, 37(12), 118-123. (In Chinese)

[20] Min, X. Q. (2003). Information asymmetry and public response in the SARS era. Journal of Nanjing University (Philosophy, Humanities and Social Sciences), 5, 125-131. (In Chinese)

[21] Zhang, S., & Guo, Z. (2023). Online public opinion crisis of public health emergencies and its governance. Journal of Beijing Jiaotong University (Social Sciences), 22(2), 133-140. (In Chinese)

[22] Lin, X. Y., & Ren, Y. H. (2021). Research on network public opinion and guidance mechanism of major public health emergencies. Academic Research, 7, 65-68. (In Chinese)

[23] Lorenz-Spreen, P., Lewandowsky, S., Sunstein, C. R., & Hertwig, R. (2020). How behavioural sciences can promote truth, autonomy and democratic discourse online. Nature Human Behaviour, 4(11), 1102-1109.

[24] Secret, Y. Q., Jiao, M., Wang, Z. D., et al. (2022). Analysis of the current situation and strategy of online public opinion response to public health emergencies in China. China Public Health, 38(12), 1600-1606. (In Chinese)

[25] Li, Z. (2021). Forecast and simulation of the public opinion on the public policy based on the Markov model. Complexity, 1-11.

[26] Xiao, W. T., & Zeng, H. L. (2017). Governmental public opinion response to emergencies: Facing posture, predicament and countermeasure ideas. China Administration, 12, 111-116. (In Chinese)

[27] Jia, N., Xia, Y., & Jia, L. (2021). Research on portrait of online public opinion subject based on big data of public opinion-A case study of Notre Dame. In Journal of Physics: Conference Series (Vol. 1861, No. 1, p. 012023). IOP Publishing.

[28] Qu, H. J. (2022). Negative emotion expression and diversion in internet public opinion of emergencies. Journal of Zhengzhou University (Philosophy and Social Sciences), 55(1), 121-125. (In Chinese)

[29] Xiao, W. T., & Huang, X. J. (2015). Exploration of the imbalance of power contrast in the online public opinion field in the all-media era. China Administration, 8, 6-12. (In Chinese)

[30] Keith, S. (2003). Cyber republic: The problem of democracy in the network society (H. M. Huang, Trans.). Shanghai People's Publishing House. (In Chinese)