Online public opinion dissemination model and simulation under media intervention from different perspectives

Geng, Lixiao; Zheng, Hui; Qiao, Gaigai; Geng, Lisha; Wang, Ke

doi:10.1016/j.chaos.2022.112959

Cited by 19 publications

(7 citation statements)

References 8 publications

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“…Analyzing the stability of dynamics model is helpful to evaluate the evolution performance of the model. The relevant dynamics model analysis methods have been widely applied in exploring the evolution laws of large-scale complex propagation systems (such as energy transfer, molecular diffusion, and information spread) [33,40]. Specifically, theoretically deducing the stability of the equilibrium points of TD-SHFOR model is helpful for us to determine the spreading trend of misinformation.…”

Section: Stability Analysismentioning

confidence: 99%

“…First, according to Lyapunov stability theorem [40], we select some parameters from the model and the state equations in the model to construct an auxiliary Lyapunov function [39] as follows:…”

Section: Stability Analysismentioning

confidence: 99%

“…First, according to Lyapunov stability theorem [40], we select some parameters from the model and the state equations in the model to construct an auxiliary Lyapunov function [39] as follows:

\begin{array}{ll}& L=\frac{\beta \left(\varepsilon +\mu \right)\left[ q\alpha S(t)F(t)+ q\xi S(t)O(t)-\left(\theta +\beta +\mu \right)H(t)\right]}{\left(\theta +\beta +\mu \right)\left(\sigma +\gamma +\mu \right)\left(\varepsilon +\mu \right)}\\ {}& \operatorname{}\kern2em +\frac{\beta H(t)-\left(\sigma +\gamma +\mu \right)F(t)}{\sigma +\gamma +\mu}\\ {}& \operatorname{}\kern2em +\frac{q\xi \beta \left[\gamma F(t)-\left(\varepsilon +\mu \right)O(t)\right]}{\left(\theta +\beta +\mu \right)\left(\sigma +\gamma +\mu \right)\left(\varepsilon +\mu \right)}\end{array}

…”

Section: Stability Analysismentioning

confidence: 99%

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A Trend Prediction Method for Misinformation Spreading with Time Delay Effect

Zhang

Lin

Wang

et al. 2023

IEEJ Transactions Elec Engng

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Online social networks (OSNs) provide a platform for users to express opinions, discuss events and exchange ideas. However, the spread of misinformation in OSNs will interfere with users' judgment of useful information and may even cause significant economic losses to society. Exploring the spreading mechanism of misinformation in online social networks is the basis of eliminating the harm brought by misinformation to the network. Firstly, considering the influence of time delay on information spreading in real life, we propose a new misinformation spreading model to explore its spreading mechanism and describe its spreading process. Secondly, we provide the predictive method for studying the spread of misinformation in OSNs and theoretically demonstrate the stability of equilibrium points in the proposed information spreading model. Finally, we conduct simulation experiments based on a real dataset. The results show that compared to the benchmark model, our proposed model can reasonably explore the spreading mechanism and accurately predict the spreading trend of misinformation. Our proposed model and method can further establish a foundation for fast and effectively controlling the spread of misinformation and improving the controllability and efficiency of network communication and information spread. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

show abstract

Section: Stability Analysismentioning

confidence: 99%

Section: Stability Analysismentioning

confidence: 99%

“…First, according to Lyapunov stability theorem [40], we select some parameters from the model and the state equations in the model to construct an auxiliary Lyapunov function [39] as follows:

\begin{array}{ll}& L=\frac{\beta \left(\varepsilon +\mu \right)\left[ q\alpha S(t)F(t)+ q\xi S(t)O(t)-\left(\theta +\beta +\mu \right)H(t)\right]}{\left(\theta +\beta +\mu \right)\left(\sigma +\gamma +\mu \right)\left(\varepsilon +\mu \right)}\\ {}& \operatorname{}\kern2em +\frac{\beta H(t)-\left(\sigma +\gamma +\mu \right)F(t)}{\sigma +\gamma +\mu}\\ {}& \operatorname{}\kern2em +\frac{q\xi \beta \left[\gamma F(t)-\left(\varepsilon +\mu \right)O(t)\right]}{\left(\theta +\beta +\mu \right)\left(\sigma +\gamma +\mu \right)\left(\varepsilon +\mu \right)}\end{array}

…”

Section: Stability Analysismentioning

confidence: 99%

See 1 more Smart Citation

A Trend Prediction Method for Misinformation Spreading with Time Delay Effect

Zhang

Lin

Wang

et al. 2023

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

show abstract

“…In terms of public opinion prediction, some scholars integrate the convolutional neural network [ 12 ], RBF neural network [ 13 ], BP neural network [ 14 ], and other models to netizens’ emotions, forwarding behaviors, and communication trends. In addition, scholars also apply neural network models to the dissemination of public opinion [ 15 ], public opinion reversal [ 16 ], and rumor detection [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

IPSO-LSTM hybrid model for predicting online public opinion trends in emergencies

Mu,

Liao,

et al. 2023

PLoS ONE

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When emergencies are widely discussed and shared, it may lead to conflicting opinions and negative emotions among internet users. Accurately predicting sudden network public opinion events is of great importance. Therefore, this paper constructs a hybrid forecasting model to solve this problem. First, this model introduces an improved inertia weight and an adaptive variation operation to enhance the Particle Swarm Optimization (PSO) algorithm. Then, the improved PSO (IPSO) algorithm optimizes the parameters of the Long Short-Term Memory (LSTM) neural network. Finally, the IPSO-LSTM hybrid prediction model is constructed to forecast and analyze emergency public opinion dissemination trends. The experimental outcomes indicate that the IPSO-LSTM model surpasses others and has high prediction accuracy. In the four emergency predictions we select, the MAPE value of IPSO-LSTM is 74.27% better than that of BP, 33.96% better than that of LSTM, and 13.59% better than that of PSO-LSTM on average. This study aims to assist authorities in quickly identifying potential public opinion crises, developing effective strategies, and promoting sustainable and positive growth in the network environment.

show abstract

“…Other studies consider the effects of online and government media on the susceptibility-Expose-infection-removal (SEIR) model [ 29 ]. The network public opinion communication models of susceptibility-expose-positive-Emotion-infection- elimination and negative-emotion-in- fection-immune-removal were constructed under the dual intervention of network media and government media [ 30 ]. The fourth is the research of network public opinion communication based on network behavior.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal pattern evolution and influencing factors of online public opinion——Evidence from the early-stage of COVID-19 in China

Wang,

Zhang,

Liu

et al. 2023

Heliyon

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Online public opinion dissemination model and simulation under media intervention from different perspectives

Cited by 19 publications

References 8 publications

A Trend Prediction Method for Misinformation Spreading with Time Delay Effect

A Trend Prediction Method for Misinformation Spreading with Time Delay Effect

IPSO-LSTM hybrid model for predicting online public opinion trends in emergencies

Spatiotemporal pattern evolution and influencing factors of online public opinion——Evidence from the early-stage of COVID-19 in China

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