政民互动数据中舆情风险预警模型研究：基于循证决策视角

doi:10.13998/j.cnki.issn1002-1248.24-0433

Abstract

Abstract:

[Purpose/Significance] The study aims to construct an early warning model of public opinion risks based on government-citizen interaction data, guided by evidence-based decision-making theory. We seek to uncover the governance value embedded in such interaction data, providing new insights and methods for identifying and managing potential public opinion risks. Traditional methods of monitoring public opinion often rely on subjective judgment, leading to potential bias and inefficiency. In contrast, this study uses objective, data-driven techniques to improve the accuracy and reliability of risk predictions. By integrating evidence-based decision making with public opinion analysis, the study not only advances the theoretical framework but also provides practical tools for government use. This innovation is significant as it addresses the gaps in the current literature regarding the objective assessment of public opinion risks and their impact on governance, thereby contributing to the field of public administration and social governance. [Method/Process] The research methodology involves a multi-step process, starting with the identification of key indicators of public opinion risks. These indicators include appeal purpose, text length, sensitivity, emotional tendency, and degree of aggregation. The analytical hierarchy process (AHP) and the criteria importance through intercriteria correlation (CRITIC) method were employed to calculate the weight of each indicator. AHP, a subjective weighting method, uses expert judgement to construct a judgement matrix and determine indicator weights. However, to reduce subjective bias, the CRITIC method is integrated, which objectively determines weights based on the variability and conflict in the data. The model's workflow began with problem identification, which captures the issues that government officials want to address through public opinion monitoring. Data were then collected from various channels, such as the "12345" government service hotline, government Weibo accounts, and official email inboxes. The risk identification phase involves the construction of a public opinion risk identification index system to identify potential risks in the data collected. This is followed by a risk assessment, where the weight of each indicator is calculated, and the risks are classified into different levels. Finally, decision recommendations were provided based on the risks identified and their urgency. The model was validated using government-citizen interaction data from Suzhou as a case study. The results of the analysis were closely aligned with the future priorities of the Suzhou municipal government, fully demonstrating the model's effectiveness and reliability of the model for early risk warning. [Results/Conclusions] The study concludes with the validation of a feasible and practical early warning model for public opinion risks. The model was tested using interaction data from the Suzhou municipal government's official website, demonstrating its effectiveness in identifying and predicting public opinion risks. The results show that the model can accurately assess the severity of risks and provide timely warnings, helping government decision-makers to manage risks proactively.

Key words: public-political interaction, public appeal, risk warning, evidence-based policy making

CLC Number:

G353

ZHANG Liman, WU Yueting, CHENG Wenjing, LIU Tianyi, SUN Xinxin. Public Opinion Risk Early Warning Model on Government-Citizen Interaction Data: A Perspective on Evidence-based Decision Making[J].Journal of library and information science in agriculture, 2024, 36(12): 89-103.

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项目	指标	参考来源
诉求主体本身	诉求目的	曹艳辉^[6]等
诉求主体本身	聚集度	邱文^[29]等
诉求内容本身	文本长度	张楠迪扬^[30]、王东琪^[31]等
诉求内容本身	情感倾向	贾改革^[7]、张莉曼^[2]等
诉求话语模式	敏感度	梅潇^[32]、SAZZED^[33]等

指标	诉求类型	文本长度	敏感度	情感倾向	聚集度
诉求类型	1	1	0.143	0.2	0.167
文本长度	1	1	0.143	0.2	0.167
敏感度	7	7	1.000	2.0	1.111
情感倾向	5	5	0.500	1.0	0.500
聚集度	6	6	0.900	2.0	1.000

项	特征向量	权重值/%	最大特征根	CI值
诉求类型	0.247	4.941	5.027	0.007
文本长度	0.247	4.941
敏感度	1.849	36.989
情感倾向	1.204	24.075
聚集度	1.453	29.054

指标	类别代码	量化方式
诉求目的	热线（1）、咨询（2）、建议（3）、投诉（4）	按照公众监督栏目中填写者自己选择的诉求目的来划分
敏感度	无敏感（1）、低敏感（2）、中度敏感（3）、高敏感（4）	在现有的评论敏感词库基础上进一步添加具有政民互动特征的敏感词（如上访等），将诉求的主要内容与新构建的敏感词库相比对，不包含任何敏感词的视为无敏感，包含1个到3个敏感词的视为低敏感，包含4个到8个敏感词的视为中度敏感，包含9个及以上的视为高度敏感
聚集度	低（1）、中（2）、高（3）	利用余弦相似度算法判断公众诉求是否反映的是同一个问题，一个问题被仅被提到1次标记为低，被提到2至3次标记为中，被提到4次及以上标记为高
情感倾向	正向（1）、中性（2）、负向（3）	依据SnowNLP对诉求的主要内容计算情感分数，该分数表示了文本的情感急性，低于0.3为负向情感，0.3~0.7为中性情感，高于0.7为正向情感
文本长度	短篇（1）、中篇（2）、长篇（3）	统计公众诉求主要内容的字数，将所有诉求的字数统计结果按照升序排序，按照3:4:3的比例将文本长度划分为短篇、中篇、长篇3类

指标名	Ahp法	Critic权重法	最终权重
敏感度	0.395 92	0.093 489	0.248 350 979
情感倾向	0.205 05	0.238 561	0.328 214 030
文本长度	0.071 12	0.284 520	0.135 769 578
聚集度	0.237 57	0.151 238	0.241 074 039
诉求目的	0.089 74	0.232 192	0.139 807 748

Public Opinion Risk Early Warning Model on Government-Citizen Interaction Data: A Perspective on Evidence-based Decision Making

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