PMID- 34744256
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230103
IS  - 0306-4573 (Print)
IS  - 0306-4573 (Electronic)
IS  - 0306-4573 (Linking)
VI  - 59
IP  - 1
DP  - 2022 Jan
TI  - Multi-stage Internet public opinion risk grading analysis of public health 
      emergencies: An empirical study on Microblog in COVID-19.
PG  - 102796
LID - 10.1016/j.ipm.2021.102796 [doi]
AB  - 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.
CI  - (c) 2021 Elsevier Ltd. All rights reserved.
FAU - Liu, Jun
AU  - Liu J
AD  - School of Safety Science and Emergency Management, Wuhan University of 
      Technology, Wuhan 430070, China.
FAU - Liu, Liyi
AU  - Liu L
AD  - School of Safety Science and Emergency Management, Wuhan University of 
      Technology, Wuhan 430070, China.
FAU - Tu, Yan
AU  - Tu Y
AD  - School of Safety Science and Emergency Management, Wuhan University of 
      Technology, Wuhan 430070, China.
FAU - Li, Shixuan
AU  - Li S
AD  - School of Safety Science and Emergency Management, Wuhan University of 
      Technology, Wuhan 430070, China.
FAU - Li, Zongmin
AU  - Li Z
AD  - School of Business, Sichuan University, Chengdu 610065, China.
LA  - eng
PT  - Journal Article
DEP - 20211026
PL  - England
TA  - Inf Process Manag
JT  - Information processing & management
JID - 9877091
PMC - PMC8556697
OTO - NOTNLM
OT  - AHPSort II-SW
OT  - COVID-19
OT  - Internet public opinion
OT  - MCDM
OT  - Public health emergencies
OT  - Risk grading
COIS- The authors declare that they have no known competing financial interests or 
      personal relationships that could have appeared to influence the work reported in 
      this paper.
EDAT- 2021/11/09 06:00
MHDA- 2021/11/09 06:01
PMCR- 2021/10/26
CRDT- 2021/11/08 06:26
PHST- 2021/06/19 00:00 [received]
PHST- 2021/09/11 00:00 [revised]
PHST- 2021/10/15 00:00 [accepted]
PHST- 2021/11/08 06:26 [entrez]
PHST- 2021/11/09 06:00 [pubmed]
PHST- 2021/11/09 06:01 [medline]
PHST- 2021/10/26 00:00 [pmc-release]
AID - S0306-4573(21)00274-0 [pii]
AID - 102796 [pii]
AID - 10.1016/j.ipm.2021.102796 [doi]
PST - ppublish
SO  - Inf Process Manag. 2022 Jan;59(1):102796. doi: 10.1016/j.ipm.2021.102796. Epub 
      2021 Oct 26.