基于改进SIR模型的反转事件舆情传播控制研究

doi:10.16182/j.issn1004731x.joss.21-0607

摘要/Abstract

摘要：

为识别恶性新闻反转事件中谣言的传播规律和更有针对性地制定引导决策，提出了一种模拟病毒信息传播的短期预测模型。改进了传统SIR(susceptible infected removed)模型，解决了以往与SD(systems dynamics)模型结合时受限于马尔可夫链而导致转化率固定单一的问题，且以“哮喘女童”事件为例，进行了数据验证。结果表明，模型不仅能有效模拟单次反转事件中的舆情传播危机，分析往期事件处理满意度、官方信息透明度和政府响应时间等控制因素对事件走向的影响，并且能够复现出传统模型中不易看到的传播特征。

关键词: 新闻反转, 舆情传播, SIR模型, 系统动力学

Abstract:

In order to identify the spreading rules of rumors in vicious news reversal events and make more targeted guiding decisions, a short-term prediction model is proposed to simulate the spread of virus information. This paper improves the traditional susceptible infected removed (SIR) model and solves the problem that the conversion rate is fixed and single due to the limitation of Markov chain when it is combined with systems dynamics (SD) model. The data is validated with the example of "asthmatic girls" . The results show that the model not only effectively simulates the crisis of public opinion communication in a single reversal event, but also analyzes the influence of control factors such as the satisfaction of previous events, the transparency of official information and the government response time on the trend of the event, and reproduces the transmission characteristics that are not easily revealed in the traditional model.

Key words: news inversion, public opinion dissemination, susceptible infected removed (SIR) model, system dynamics

中图分类号:

TP391.9

唐建荣, 鲍佳彤. 基于改进SIR模型的反转事件舆情传播控制研究[J]. 系统仿真学报, 2022, 34(11): 2406-2415.

Jianrong Tang, Jiatong Bao. Research on Network Public Opinion Transmission Mechanism of Inversion Event Based on Integrating Improved SIR Model[J]. Journal of System Simulation, 2022, 34(11): 2406-2415.

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变量类型	变量名称	变量表达式
水平变量	易感人群S	Integ(-直接免疫转化速率-不实新闻感染传播速率，20 000)
	感染传播人群I	integ(不实新闻感染传播速率-政府管控处理速率，0)
	感染康复人群R(含免疫者)	integ(直接免疫转化速率+政府管控处理速率，0)
速率变量	不实新闻感染传播速率^[15]	易感人群S×感染比例×观点碰撞度
	政府管控处理速率^[15]	感染传播人群I×(政府公信力+政府关注度)/感染传播平均持续时间d
	直接免疫转化速率	易感人群S×直接免疫比例×质疑度
辅助变量	政府关注度^[27]	0.5×事件影响力+0.5×网络舆情热度
	政府公信力^[27]	(事件处理满意度-政府响应时间×5)×官方信息透明度/2
	网民参与度^[28]	1-0.348×exp(-0.000 083×易感人群S)
	网络舆情热度^[28]	1-0.953×exp(-0.000 422×感染传播人群I)

官方信息透明度	往期事件处理满意度	MAPE/%	备注
0.5^[31]	7^[31]	88.85
	7.377^[28]	48.67
	1.923^[34]	>100	不符合模型约束条件，舍去
0.68^[32]	7^[31]	33.99
0.68^[32]	7.377^[28]	21.97
0.7^[31]	7^[31]	31.18
0.7^[31]	7.377^[28]	20.93
0.73^[28]	7^[31]	25.73
0.73^[28]	7.377^[28]	19.55
0.75^[33]	7^[31]	18.78	MAPE值最小，得最优参数值
0.75^[33]	7.377^[28]	26.61