暴雨灾害短视频引发舆情危机的BN研究

doi:10.16265/j.cnki.issn1003-3033.2022.11.0077

摘要/Abstract

摘要：

为减少短视频引发的网络舆情危机风险,以“7·20”河南暴雨灾害为例,基于网络短视频,从突发事件的灾情程度、应对行为、视频属性、舆情情感倾向和舆情传播扩散度5个方面,构建短视频网络舆情危机风险的预测指标体系,利用贝叶斯网络(BN)构建预测模型,探讨舆情预测准确度。结果表明:经检验测试,该模型可有效预测网络舆情危机风险等级,公众面对突发事件时的应急响应行为对舆情危机风险等级、情感倾向和传播扩散度均有重要影响;在灾情严重且公众的行为应对较消极的情境下,舆情爆发的主因为公众的情感倾向和舆情的传播扩散度;相较于微博转发量和点赞量,评论量高的微博短视频更容易引发网络舆情危机。

关键词: 暴雨灾害, 网络短视频, 舆情危机风险, 贝叶斯网络(BN), 舆情预测

Abstract:

In order to reduce the risk of network public opinion crisis caused by short videos, taking the "7·20" Henan rainstorm incident as an example, this paper constructed a prediction index system for the risk of public opinion crisis based on short-form online videos from five aspects:the degree of disaster, response behavior, video attributes, public opinion sentiment tendency and public opinion spread diffusion of the unexpected incident. BN was used to build a prediction model to discuss the accuracy of public opinion forecast. The validation results indicate that the model can effectively predict the risk level of network public opinion crisis, and the public's response behavior in the face of emergencies has an important influence on the risk level of public opinion crisis, sentiment tendency and the spread of public opinion. When the disaster is severe and the public's behavior is relatively negative, the main reason for the outbreak of public opinion is the public's emotional tendency and the spread of public opinion. Short videos with a high number of comments are more likely to trigger network public opinion crises than retweets and likes.

Key words: rainstorm disaster, online short video, public opinion crisis risk, Bayesian network (BN), public opinion forecast

吕伟, 周雯楠, 陈文涛, 韩业凡, 房志明. 暴雨灾害短视频引发舆情危机的BN研究[J]. 中国安全科学学报, 2022, 32(11): 192-199.

LYU Wei, ZHOU Wennan, CHEN Wentao, HAN Yefan, FANG Zhiming. Research on BN of network public opinion crisis risk caused by short videos of rainstorm disaster[J]. China Safety Science Journal, 2022, 32(11): 192-199.

图/表 8

表1

图1

表2

网络舆情危机风险等级预测BN边缘概率

序号	S₀	S₁₁	S₁₂	$…$	S₅₄
视频1	低	高	高	$…$	高
视频2	中	高	高	$…$	高
视频3	中	低	高	$…$	高
$︙$	$︙$	$︙$	$︙$		$︙$
视频224	中	中	高	$…$	低

表2

表3

图2

表4

表5

图3

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目标变量	一级指标	二级指标	描述	取值
网络舆情危机风险S₀	灾情程度S₁	水位S₁₁	膝盖以下、膝盖以上臀部以下、臀部以上	低、中、高
	灾情程度S₁	受灾人数S₁₂	(0,5)、[5,20]、(20,+∞)	低、中、高
	应对行为S₂	被困者积极自救S₂₁	隶属、不隶属	是、否
		参与营救他人S₂₂	隶属、不隶属	是、否
		捐赠物资S₂₃	隶属、不隶属	是、否
		感恩救援人员S₂₄	隶属、不隶属	是、否
		发布救援信息S₂₅	隶属、不隶属	是、否
		服从转移安置S₂₆	隶属、不隶属	是、否
		非理性行为S₂₇	隶属、不隶属	是、否
	视频属性S₃	互助救助类S₃₁	隶属、不隶属	是、否
		寻求帮助类S₃₂	隶属、不隶属	是、否
		受灾现场类S₃₃	隶属、不隶属	是、否
		非理性行为类S₃₄	隶属、不隶属	是、否
	舆情情感倾向S₄	微博原文情感倾向S₄₁	情感值大于0、情感值小于等于0	积极、消极
	舆情情感倾向S₄	微博评论情感倾向S₄₂	积极情感占比大于50%、积极情感占比小于等于50%	积极、消极
	舆情传播扩散度S₅	信息发布主体S₅₁	官方媒体、自媒体	官媒、自媒体
		微博评论总数S₅₂	(0,300]、(300,700]、(700,1000]	低、中、高
		微博转发总数S₅₃	(0,300]、(300,700]、(700,1000]	低、中、高
		微博点赞总数S₅₄	(0,300]、(300,700]、(700,1000]	低、中、高

节点	排名前5 的节点	相关度指标	占比	相关度指标变异系数
S₀	S₂	0.187 85	15.3	0.048 490 9
	S₅	0.085 05	6.95	0.023 714 9
	S₄₂	0.044 08	3.6	0.003 486 3
	S₁	0.041 63	3.4	0.005 809 1
	S₄₁	0.030 14	2.46	0.002 423 1
S₄	S₂	0.347 34	35.5	0.109 097 7
	S₁	0.015 74	1.61	0.005 196 1
	S₄₂	0.015 33	1.57	0.005 237 8
	S₄₁	0.011 13	1.14	0.003 805 0
	S₂₅	0.007 67	0.783	0.002 513 3
S₅	S₀	0.085 05	5.38	0.012 373 7
	S₅₂	0.031 15	1.97	0.004 661 0
	S₂	0.014 39	0.911	0.002 248 7
	S₅₃	0.014 27	0.904	0.001 956 7
	S₅₄	0.007 32	0.463	0.001 084 9

节点	状态	后验推理1			后验推理2
节点	状态	预测模型 S₀=低 (p=56.1)	后验概率1 S₀=高 (p=100)	变动趋势	后验概率1 S₀=高 (p=100)	后验概率2 S₀=高、S₁=高、S₂=消极 (p=100)	变动趋势
S₁	低	22.3	30.7	37.67	30.7	—	—
	中	29.0	52.2	80.00	52.2	—	—
	高	48.6	17.0	-65.02	17.0	—	—
S₂	消极	37.3	21.0	-43.70	21.0	—	—
	中立	29.5	29.8	1.02	29.8	—	—
	积极	33.1	49.3	48.94	49.3	—	—
S₃	消极	26.7	35.1	31.46	35.1	38.3	9.12
	中立	35.5	31.8	-10.42	31.8	30.9	-2.83
	积极	37.7	33.1	-12.20	33.1	30.8	-6.95
S₄	消极	58.6	38.7	-33.96	38.7	100	158.40
S₄	积极	41.4	61.3	48.07	61.3	0	-100
S₅	低	36.2	36.2	0.00	36.2	0	-100
	中	29.5	23.4	-20.68	23.4	18.9	-23.81
	高	34.3	40.4	17.78	40.4	81.1	100.74