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A dynamic dissemination model for recurring online public opinion

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Abstract

With an increasing number of people sharing feelings and opinions online, the online platforms have become one of the most important channels for public opinion dissemination. Moreover, recurring online public opinion has become a primary form of online public opinion and has begun to have major effects on prompting social change. Therefore, this paper establishes a novel dynamic dissemination model to systematically study the recurrence of online public opinion. Through an in-depth analysis, three major influencing factors are determined, a recurrence probability function is identified, and then a SIR-I-based dynamic dissemination model is successfully developed, for which the uniformly asymptotically stability is fully proved. A case study from “Child abuse in Ctrip kindergarten” is conducted to demonstrate the validity of the proposed model. The parameter analysis proved that controlling of the public opinion heat, control effectiveness, event topic relevance, and recurrence time point is an effective way to manage the recurrence dissemination, and that opinion leaders play an important role in dissemination. Meanwhile, comparative analysis shows that our model efficiently characterized the dissemination process of recurring online public opinion. As our paper expanded the research cycle of public opinion to its recurrence, it not only enriches online public opinion dissemination model development, but also provides a reference for quantitative analysis of recurring online public opinions dissemination.

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Abbreviations

t :

Time

m :

Recurrence time point

\(y_1\) :

Public opinion heat

\(y_2\) :

Control effectiveness

\(y_3\) :

Event topic relevance

\(E_i\) :

Event i

ce :

Control effectiveness score

\(ce_1\) :

Rapid reaction level

\(ce_2\) :

Disposal guidance level

\(ce_3\) :

Legalization level

\(ce_{11}\) :

Information publicity degree

\(ce_{12}\) :

Response speed

\(ce_{21}\) :

Channelization

\(ce_{22}\) :

Authority of spokesperson

\(ce_{211}\) :

Network interaction level

\(ce_{212}\) :

Understanding the demands level

\(ce_{31}\) :

Law enforcement level

\(ce_{32}\) :

Standardization level

v :

The weight of influencing factor

P :

Equilibrium point

\({\mathfrak {R}_0}\) :

Dissemination threshold

y :

Overall level of factors influencing online public opinion recurrence

\(WV({E_i})\) :

Online public opinion-related feature word vectors for event i

\({t_g}\) :

The gth feature word in the VSM

\(w_{{E_i}}^g\) :

The weight of \({t_g}\) in event i

r(t):

Recurring online public opinion function

k :

Adjustment coefficient

N :

Number of netizens

S :

The ignorant

I :

The spreaders

R :

The stiflers

S(t):

Proportion of the ignorant in the population at time t

I(t):

Proportion of spreaders in the population at time t

R(t):

Proportion of stiflers in the population at time t

\(S_0\) :

Proportion of the ignorant in the population at time 0

\(I_0\) :

Proportion of spreaders in the population at time 0

\(R_0\) :

Proportion of stiflers in the population at time 0

a :

Spreading rate

b :

Stifling rate

V :

Lyapunov function

\(\hat{I}_{t}\) :

Simulation values vector of I

\({I_t}\) :

Actual values vector of I

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Acknowledgements

This work was supported by the Research on Social Public Opinion Management of Urban Disaster Events under the Background of Big Data, PR China, the National Social Science Fund Major Bidding Project (No. 17ZDA286) and the Basic Research Business Expenses Project for the Central Universities of Sichuan University (No. skzd2018-pt06). The authors would like to thank the anonymous referees for their insightful comments and suggestions to improve this paper, as well as the Uncertainty Decision-Making Laboratory of Sichuan University for helpful comments and discussion.

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Authors and Affiliations

  1. Business School, Sichuan University, Chengdu, 610064, People’s Republic of China

    Jiuping Xu, Weiyao Tang, Yi Zhang & Fengjuan Wang

  2. School of Economics and Management, Yibin University, Yibin, 644007, People’s Republic of China

    Yi Zhang

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Xu, J., Tang, W., Zhang, Y. et al. A dynamic dissemination model for recurring online public opinion. Nonlinear Dyn 99, 1269–1293 (2020). https://doi.org/10.1007/s11071-019-05353-3

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