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Data Analysis and Knowledge Discovery  2023, Vol. 7 Issue (8): 17-29    DOI: 10.11925/infotech.2096-3467.2022.0866
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Review of Early Warning for Online Public Opinion
Di Luyang1,Zhong Han1(),Shi Shuicai2
1School of Information and Cyber Security, People’s Public Security University of China, Beijing 102600, China
2TRS Information Technology Co., Ltd., Beijing 100101, China
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Abstract  

[Objective] This paper summarizes the developments of early warning research for online public opinion. [Coverage] We searched the Web of Science and CNKI with related terms such as public opinion warning, online public opinion, and public opinion risks. A total of 52 articles representing the foundations of the disciplines and the development trends were selected for a comprehensive review. [Methods] We summarized the foundations of early warning studies from the perspective of online public opinion characteristics and risk evaluations. Then, we examined the status quo of current research on early warning for online public opinion. [Results] Currently, most research focuses on expert empowerment, machine learning, communication process, and sentiment analysis. All of them can accurately predict the risk level of online public opinion, which is significant to the online environment and social stability. [Limitations] More research is needed to review early warning technology. [Conclusions] The existing research does not provide universal concepts for online public opinion. The risk evaluation method needs to be improved. We should also establish authoritative and unified standards to compare the performance of different monitoring systems.
Key words： Online Public Opinion      Public Opinion Monitoring      Risk Assessment      Public Opinion Warning     
Received: 15 August 2022      Published: 22 March 2023
ZTFLH:  TP391  
Fund:Fundamental Research Funds for People’s Public Security University of China(2022JKF02018);National Social Science Fund of China(20AZD114)
Corresponding Authors: Zhong Han，E-mail：zhonghan@ppsuc.edu.cn。   
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Luyang Di
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Cite this article:

Di Luyang, Zhong Han, Shi Shuicai. Review of Early Warning for Online Public Opinion. Data Analysis and Knowledge Discovery, 2023, 7(8): 17-29.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022.0866     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2023/V7/I8/17

Network Public Opinion Early Warning Workflow
Annual Publication Statistics of Public Opinion Monitoring
一级指标 二级指标
网络舆情主题E1 网络舆情主题关注度E11
网络舆情主题敏感度E12
网络舆情主题时效度E13
网络舆情发布者E2 舆情发布者基本状况E21
舆情发布者所在平台E22
舆情发布者关注人数E23
网络舆情参与者E3 舆情参与者基本状况E31
舆情参与者所在平台E32
舆情参与者关注人数E33
网络舆情传播E4 舆情传播平台权威度E41
舆情传播平台分布度E42
舆情传播速度E43
意见领袖影响力E44
The Fuzzy Comprehensive Evaluation Index System
一级指标 次级指标 次级指标含义
主体维E1 负面回帖量维E11 表示舆情的负面评论数，反映网民参与度
历史阅览量维E12 表示舆情的总体浏览量，反映网民关注度
传播维E2 持续时间维E21 表示舆情从萌芽期到消亡所持续的总天数，反映其时效度
发帖数量维E22 表示舆情的原创发布与转发帖的总数量
媒体平台数量维E23 表示舆情扩散的主流媒体数量，反映其分布度
内容维E3 主题敏感度维E31 表示舆情的主题敏感性，根据内容分为6个不同方面
视听化程度维E32 表示舆情文字以外的多模态表征程度
Network Public Opinion Early Warning Index System Based on Security System Dimension Reduction Theory
赋权理论 作者 时间 主要工作内容
层次分析法
（Analytic Hierarchy Process，AHP）		 张艳丰等[38] 2017 用改进后的AHP计算指标权重，基于用二级综合模糊评价求出的语义隶属度实现对网络舆情的监测预警
杨小溪等[39] 2021 将指标划分为三个层次，通过专家打分法得到权值，再应用多层次模糊评价法进行实例验证，证明了该体系实用性
王英杰等[40] 2021 将层次分析与专家调查法相结合，计算得到指标权重。随后经实例检验了所构建指标体系的可用性，为预警工作提供支撑
熵权法 武慧娟等[7] 2018 使用熵权法计算各级指标权重，构建基于熵权法的模糊综合评价模型，从而确定风险等级
网络分析法
（Analytic Network Process， ANP）		 陈培友等[8] 2019 基于ANP构建网络层次结构模型，以1~9尺度理论确定指标权重，再基于灰色系统理论构建模糊综合矩阵，以此判定综合评价结果
田世海等[41] 2021 采用1~9标度法，基于ANP得到指标权重，再结合随机Petri 网与同构的马尔科夫链进行定量分析，实现预警目的
投影法 林玲等[42] 2021 利用直觉模糊Choquet积分算子并融入风险偏好，在直觉模糊评价矩阵基础上得到专家评分矩阵，再引入投影法计算指标权重，求得最终评价结果
Early Warning Method Based on Artificial Weighting
主要理论基础 作者 时间 主要工作内容
加速遗传算法（AcceleratingGenetic Algorithm， AGA）
投影寻踪（Projection Pursuit，PP）		 黄星等[43] 2018 从突发事件网络舆情中提取指标，用加速遗传算法优化投影指标函数，在最佳投影方向得到投影值，使用Logistics Cure进行拟合，得到的拟合值与实际预警等级接近
Logistic模型 连芷萱等[9] 2018 通过微博舆情信息构建确定等级的案例数据库，对其做逻辑回归，再将实时舆情信息处理后代入得到的关系式，推导得到预警等级
Logistic模型
灰色预测模型		 连芷萱等[44] 2019 用结合Logistic模型、指数平滑模型、灰色模型的组合模型计算出原始舆情消退期。在此基础上计算实时舆情衍生系数，确定预警等级
OCS（One Class-SVM）异常检测模型
灰色预测模型		 祁凯等[45] 2019 首先用OCS模型捕获社交平台的异常数据，再基于EGM灰色预测模型对该舆情的发展趋势进行判别，最后根据预测结果得到预警评级
社会安全阀理论
灰色预测模型		 章留斌等[46] 2020 基于社会安全阀理论对微博数据展开分析，并结合灰色系统理论构建预警模型，求出舆情信息的基准值，并根据该结果划分预警等级
灰色关联分析
支持向量机		 杨柳等[6] 2020 采用灰色关联分析和K-Means聚类分析，将舆情信息归类分级，最后用支持向量机算法实现舆情风险自动识别
支持向量机 袁媛[47] 2022 用主成分分析，对从舆情信息中提取的指标进行处理，并将量化结果输入SVM模型，输出结果为风险预警估值
贝叶斯网络 罗文华等[48] 2021 根据网络舆情发酵实例，建立贝叶斯网络多级次发酵预警模型，并结合MPE原理对发酵原因进行诊断
Early Warning Method Based on Machine Learning
演化模型 作者 时间 研究方法
SIS模型 周琦萍等[49] 2019 分析SIS模型与舆情实际扩散过程，在传播特征的基础上得到舆情爆发临界时刻，结合分析舆情扩散系数以及遗忘率，建立无监督的舆情传播监控预警机制
SIER模型 孙蕾等[50] 2019 基于SIER模型建立正向传播与反向传播的IER模型并讨论边界值情况。通过建立社会风险感知函数，主要研究了舆情传播方向、舆情状态间转化率及社会风险之间的相互影响关系
SIR-EGM模型 彭程等[51] 2020 基于SIR传染病模型与EGM灰色预测模型，提出一种实现舆情预警与舆情防控模型，并利用Python挖掘到的政务微博历史数据进行模型模拟与检验
SIRS模型 万立军等[52] 2021 结合SIRS模型与舆情传播过程构建其演化模型，以预警为目的分析不同阶段的特征。利用灰色预测方法与马尔科夫模型对发展趋势进行预测，实现舆情风险预警，并基于结果划分等级
Network Public Opinion Early Warning Method Based on Communication Process
网络舆情预警等级 表现形式
严重度 关注度 传播速度 影响范围 转化为行为舆论
Ⅳ（蓝色）级 轻度 低 慢 较小范围 没有可能
Ⅲ（黄色）级 中度 较高 中等 局限在一定范围 基本没有可能
Ⅱ（橙色）级 重度 高 快 扩散到很大范围，境外媒体开始参与 有转化的可能
Ⅰ（红色）级 特重 极高 极快 影响到整个社会，境外媒体高度关注 有且即将转化
Classification of Early Warning Level of Network Public Opinion
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