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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2026, Vol. 60 Issue (6): 1261-1268    DOI: 10.3785/j.issn.1008-973X.2026.06.013
    
Network public opinion risk warning method integrating personality trait
Hao LIN1,2(),Leixiao LI1,2,*(),Li ZHAO3
1. College of Intelligent Science and Technology (College of Cyberspace Security), Inner Mongolia University of Technology, Hohhot 010080, China
2. Inner Mongolia Key Laboratory of Beijiang Cyberspace Security, Hohhot 010080, China
3. School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
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Abstract  

Online public opinion early warning indicator incorporating personality trait information was proposed based on personality psychology and the new social analysis model in order to enhance the effectiveness and interpretability of online public opinion risk early warning method. A social network public opinion early warning indicator system which comprised 4 first-level indicators and 18 second-level indicators was established by combining mainstream indicator such as event topics, public attitude and sentiment tendency. The entropy weight method was employed to determine the weight of each indicator, and TOPSIS was utilized for comprehensive evaluation of public opinion risk. An improved grey wolf optimizer-enhanced N-BEATS model was used to predict public opinion risk at the next moment. Four real-world events were analyzed using the proposed approach in order to verify the feasibility of the proposed early warning method. Factor analysis results show that personality trait-related indicator is more significant compared with sentiment tendency indicator. The prediction results demonstrate that the proposed early warning method can effectively fit risk value sequence and accurately predict public opinion risk.

Key words： network public opinion      public opinion risk warning      personality trait      time series prediction     
Received: 07 July 2025      Published: 06 May 2026
CLC:  G 353  
  G 202  
  TP 39  
Fund:  国家自然科学基金资助项目（62362055）；自治区首批“五大任务”关键技术研究专项资助项目（NMGWDRW2025-03）；内蒙古自治区重点研发与成果转化计划资助项目（2024SKYPT0012）；内蒙古自治区高等学校青年科技英才支持计划资助项目（NJYT22084）；内蒙古自然科学基金资助项目（2023MS06008）.
Corresponding Authors: Leixiao LI     E-mail: suzukaze_aoba@126.com;llxhappy@126.com
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Hao LIN
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Cite this article:

Hao LIN,Leixiao LI,Li ZHAO. Network public opinion risk warning method integrating personality trait. Journal of ZheJiang University (Engineering Science), 2026, 60(6): 1261-1268.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2026.06.013     OR     https://www.zjujournals.com/eng/Y2026/V60/I6/1261


融入人格特质的网络舆情风险预警方法

为了提高网络舆情风险预警方法的有效性和可解释性，以人格心理学和新社会分析模型为基础，提出融入人格特质信息的网络舆情预警指标. 结合事件主题、公众态度、情感倾向等主流指标，建立包含4个一级指标、18个二级指标的社交网络舆情预警指标体系. 采用熵权法确定各指标权重，利用TOPSIS综合评估舆情风险. 利用改进的灰狼算法优化的N-BEATS预测下一时刻的舆情风险. 为了验证该预警方法的可行性，采用所提方法预测分析4个真实事件. 因子分析结果表明，与情感倾向指标相比，提出的人格特质相关指标更重要. 预测结果表明，利用该预警方法能够拟合风险值序列，可以精确预测舆情风险.


关键词： 网络舆情,  舆情风险预警,  人格特质,  时间序列预测 
Fig.1 Network public opinion and neo-socioanalytic model
Fig.2 Relationship model between Big Five personality trait and social media behavior
Fig.3 Proposed risk warning method for online public opinion
Fig.4 Network public opinion risk warning index system incorporating personality trait
预警等级风险评估值表述
Ⅰ级0~0.25网民关注度低，舆情传播速度慢，舆情影响局限在较小范围内，没有转化为行为舆论的可能
Ⅱ级0.25~0.50网民关注度中等，舆情传播速度中等，舆情影响局限在一定范围内，基本没有转化为行为舆论的可能
Ⅲ级0.50~0.75网民关注度高，舆情传播速度快，境外媒体可能关注，舆情影响范围很大，有转化为行为舆论的可能
Ⅳ级0.75~1.00网民关注度极高，境外媒体可能高度关注，舆情传播速度极快，舆情影响扩大到了整个社会，
有很大可能即将化为行为舆论
Tab.1 Risk warning level of online public opinion
Fig.5 Architecture of N-BEATS network
参数取值范围
$ {t}_{\rm{SL}} $的长度系数$ {N}^{\text{predict}} $$ {N}^{\text{predict}}\in \left\{2,3,4,5,6,7\right\} $
前4层隐含层单元
数量$ {n}_{\rm{HU}} $		$ {n}_{\rm{HU}}\in \left[10，500\right]，{n}_{\rm{HU}}\in {\bf{N}}^{+} $
有2层隐含层单元
数量$ {n}_{\rm{TD}} $		$ {n}_{\rm{TD}}\in \left[1，20\right]，{n}_{\rm{TD}}\in {\bf{N}}^{+} $
N-BEATS中Stake
数量$ {n}_{\rm{SN}} $		$ {n}_{\rm{SN}}\in \left\{0,1,2,3,4,5,6,7,8\right\} $
Block类型$ {\alpha }_{\rm{BT}} $$ {\alpha }_{\rm{BT}}\in \left\{0,1,2\right\} $
Stake中Block的
数量$ {n}_{\rm{BPS}} $		$ {n}_{\rm{BPS}}\in \left[1，10\right]，{n}_{\rm{BPS}}\in {\bf{N}}^{+} $
权重$ {\alpha }_{\text{share}} $$ {\alpha }_{\text{share}}\in \left\{0,1\right\} $
批大小$ {n}_{\rm{BS}} $$ {n}_{\rm{BS}}\in \left[8，512\right]，{n}_{\rm{BS}}\in {\bf{N}}^{+} $
学习率$ {r}_{{\mathrm{l}}} $$ {r}_{\rm{l}}\in \left[0.000\;1,\;0.1\right] $
权重衰减系数$ {r}_{\rm{WD}} $$ {r}_{\rm{WD}}\in \left[0,1.0\right] $
一阶矩估计的指数
衰减率$ {r}_{\rm{EDR}} $		$ {r}_{\rm{EDR}}\in \left[0,1.0\right] $
二阶矩估计的指数
衰减率$ {{{r}^{\prime}}}_{\rm{EDR}} $		$ {{{r}^{\prime}}}_{\rm{EDR}}\in \left[0,1.0\right] $
Tab.2 N-BEATS’s hyperparameter that requires optimization
社交网络舆情案例舆情
类型		开始时间结束时间社交
媒体
延迟法定退休年龄改革政治2024-09-102024-09-15微博
三只羊美诚月饼事件社会2024-09-122024-09-27微博
上海大学生歌词歧视农民工社会2024-08-102024-08-18微博
《Mirror 2》游戏内容阉割文化2022-08-012022-12-31Steam
Tab.3 Data description of real network public opinion case
Fig.6 Weight corresponding to each case obtained using entropy weight method
社交网络舆情案例评论数转发数评论正面比例/%正面极性风险峰值预警等级
延迟法定退休年龄改革10485010798266.250.9730.680Ⅲ级
三只羊美诚月饼事件10790010478050.370.9740.328Ⅱ级
上海大学生歌词歧视农民工11512320739.690.9440.236Ⅰ级
《Mirror 2》游戏内容阉割7628—20.920.9160.153Ⅰ级
Tab.4 Warning result of real network public opinion case
Fig.7 Fitting and prediction of risk value for each case using DIGWO-N-BEATS
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