Topological evolution of the internet public opinion

The internet often serves as a communication platform for users to freely express opinions. However, as it supports the quick and wide spread of public opinions, it often results in great uncertainty and is very difficult to control. As a result, evaluating the risk levels of internet public opinion becomes very challenging. The safety of internet public opinion affects the sustainability and stability of society. In this paper, a novel conceptual model of internet public opinion governance is proposed. The model can be used to grade risk levels by combining the Analytic Hierarchy Process Sort II (AHPSort II) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) under a fuzzy environment with triangular fuzzy sets. A numerical example is provided to verify the efficiency of the proposed method, and some managerial implications are also discussed.

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“…Step 6. Calculate the value of R, C, R + C and R − C by Equations (22) and (23), as shown in Table 6. Step 7.…”

Section: Determination Of Criteria Weightsmentioning

confidence: 99%

Internet Public Opinion Risk Grading under Emergency Event Based on AHPSort II-DEMATEL

Mei

Xie

et al. 2019

Sustainability

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“…While the online public opinion dissemination is the focus of scholars' concern, researchers establish a time delay differential model (Zhang and Wang, 2017) and social judgment based opinion (SJBO) dynamic model (Fan and Pedrycz, 2017) to simulate the public opinion evolution, consider the populist opinions (Fang et al, 2019) and social media (Wang et al, 2019b) to analyze the evolution of netizens' opinions, explore the transient opinion profiles by mean-field analysis and simulations with individual trust changes (Xiong et al, 2017), conduct a simulation of transmission through the social network theory using a classic SIR epidemic model (Li et al, 2020), propose a damped oscillator model (DOM) to measure the self-decaying process of human emotion (Dong et al, 2020), propose a multiagent-based simulation model to excavate the impact of the coordination of four factors cyber citizens, opinion leaders, the government and mass media on public panic (Yu et al, 2017), establish a model to describe the topology of the Internet public opinion based on the complex network theory and reflect the degree distribution of Internet public opinion, which follows an analogous power law distribution (Lian et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

G-GERT network model of online public opinion reversal based on kernel and grey degree

Yan

Zeng

Xiong

et al. 2021

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PurposeIn recent years, online public opinion reversal incidents have been occurring frequently, which has increased the complexity of the evolution of online public opinion, and they have become a difficult issue for public opinion management and control. It is of great significance to explore the regularity of online public opinion reversal.Design/methodology/approachCombined with the grey characteristics of online public opinion information, a grey graphical evaluation review technique (G-GERT) network model is constructed based on kernel and grey degree, and the frequency, probability and time of online public opinion reversal nodes are calculated using C-marking method and Z-marking method.FindingsThroughout the online public opinion reversal events, there are all repeated outbreak nodes occurring, so the authors regard the repeated occurrence of outbreak nodes as reversal. According to the average frequency, probability and time of repeated outbreak nodes in the G-GERT network model, the authors predict the corresponding key information of reversal. It can simulate the evolution process of public opinion events accurately.Originality/valueThe G-GERT network model based on kernel and grey degree reveals the regulation of public opinion reversal, predicts the frequency, probability and time of reversal nodes, which are the most concerned and difficult issues for decision-makers. The model provides the decision basis and reference for government decision-making departments.

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“…Public opinion monitoring is a key issue in modern society, as it can provide opportune information for analyzing and predicting the trends and patterns of unexpected events on the Internet to prevent social conflicts [ 1 , 2 , 3 ]. In recent years, online information on emergencies has attracted great public attention.…”

Section: Introductionmentioning

confidence: 99%

Early Warning Scheme of COVID-19 related Internet Public Opinion based on RVM-L Model

Zhu

Ding

et al. 2021

Sustainable Cities and Society

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Internet public opinion is affected by many factors corresponding to insufficient data in the very short period, especially for emergency events related to the outbreak of coronavirus disease 2019 (COVID-19). To effectively support real-time analysis and accurate prediction, this paper proposes an early warning scheme, which comprehensively considers the multiple factors of Internet public opinion and the dynamic characteristics of burst events. A hybrid relevance vector machine and logistic regression (RVM-L) model is proposed that incorporates multivariate analysis, which adopts Lagrange interpolation to fill in the gaps and improve the forecasting effect based on insufficient data for COVID-19-related events. In addition, a novel metric critical interval is introduced to improve the early warning performance. Detailed experiments show that compared with existing schemes, the proposed RVM-L-based early warning scheme can achieve the prediction accuracy up to 96%, and the intervention within the critical interval can reduce the number of public opinions by 60%.

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Topological evolution of the internet public opinion

Cited by 24 publications

References 39 publications

Internet Public Opinion Risk Grading under Emergency Event Based on AHPSort II-DEMATEL

Internet Public Opinion Risk Grading under Emergency Event Based on AHPSort II-DEMATEL

G-GERT network model of online public opinion reversal based on kernel and grey degree

Early Warning Scheme of COVID-19 related Internet Public Opinion based on RVM-L Model

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