How local outbreak of COVID-19 affect the risk of internet public opinion: A Chinese social media case study.

Motivated by the realistic demand of controlling the Internet public opinion risk caused by the local outbreak of COVID-19, this paper creatively proposes a COVID-19 local outbreak Internet public opinion risk grading research framework. The SMAA-FAHPSort II method combining Analytic Hierarchy Process Sort II (AHPSort II) method with Stochastic Multicriteria Acceptability Analysis (SMAA-2) method is introduced into this framework, to evaluate the Internet public opinion risk level of social media during the local outbreak of COVID-19. In addition, this framework is applied to a case of Internet public opinion risk evaluation on Microblog platform of China. According to the number of new cases per day in mainland China, this paper divides the period from May 7, 2020 to September 3, 2021 into seven stages. A total of more than 10,000 Microblog hot topics were collected, after screening and preprocessing, 5422 related topics are remained to help complete the Internet public opinion risk evaluation. The case study analysis results show that the number of days classified as moderate risk and above has reached more than 280. This proves that the local outbreak of COVID-19 will indeed increase the risk of Internet public opinion, and correlation analysis confirms that the level of public opinion risk is positively correlated with the severity of the epidemic in the real world. Furthermore, the effectiveness and advantages of the proposed method are verified by comparative analysis and sensitivity analysis. Finally, some effective public opinion management suggestions have been put forward. This paper can provide reference for the government to formulate or improve relevant strategies, and also has great significance for reducing the risk of Internet public opinion in social media.

Multi-stage Internet public opinion risk grading analysis of public health emergencies: An empirical study on Microblog in COVID-19.

In the period of Corona Virus Disease 2019 (COVID-19), millions of people participate in the discussion of COVID-19 on the Internet, which can easily trigger public opinion and threaten social stability. This paper creatively proposes a multi-stage risk grading model of Internet public opinion for public health emergencies. On the basis of general public opinion risk grading analysis, the model continuously pays attention to the risk level of Internet public opinion based on the time scale of regular or major information updates. This model combines Analytic Hierarchy Process Sort II (AHPSort II) and Swing Weighting (SW) methods and proposes a new Multi-Criteria Decision Making (MCDM) method - AHPSort II-SW. Intuitionistic fuzzy number and linguistic fuzzy number are introduced into the model to evaluate the criteria that cannot be quantified. The multi-stage model is tested using more than 2,000 textual data about COVID-19 collected from Microblog, a leading social media platform in China. Seven public opinion risk assessments were conducted from January 23 to April 8, 2020. The empirical results show that in the early COVID-19 outbreak, the risk of public opinion is more serious on macroscopic view. In details, the risk of public opinion decreases slowly with time, but the emergence of important events may still increase the risk of public opinion. The analysis results are in line with the actual situation and verify the effectiveness of the method. Comparative analysis indicates the improved method is proved to be superior and effective, sensitivity analysis confirms its stability. Finally, management suggestions was provided, this study contributes to the literature on public opinion risk assessment and provides implications for practice.