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, risk warning, evidence-based policy making

CLC Number:

G353

Liman ZHANG, Yueting U, Wenjing CHENG, Tianyi LIU, Xinxin SUN. 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, (): 1-15.

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References 39

1	中国政府网. 国务院关于加强数字政府建设的指导意见[EB/OL]. [2024-03-15].
2	张莉曼, 吴鹏, 尹熙成, 等. 政民互动数据中公众诉求的故事化描述: 集成、重构与叙事[J]. 情报理论与实践, 2023, 46(4): 141-149.
	ZHANG L M, WU P, YIN X Z, et al. Storytelling description of public appeal in government-to-citizen interaction data: Integration, reconstruction and narration[J]. Information studies: Theory & application, 2023, 46(4): 141-149.
3	马亮, 郑跃平, 张采薇. 政务热线大数据赋能城市治理创新: 价值、现状与问题[J]. 图书情报知识, 2021, 38(2): 4-12, 24.
	MA L, ZHENG Y P, ZHANG C W. The big data empowering effect of government hotlines on city governance innovation: Value, status and issues[J]. Documentation, information & knowledge, 2021, 38(2): 4-12, 24.
4	张莉曼, 张向先, 孙绍丹. 分布式认知视角下政民互动数据的交互叙事研究[J]. 图书情报工作, 2023, 67(9): 53-62.
	ZHANG L M, ZHANG X X, SUN S D. Research on interactive narrative of government-citizen interaction data from the perspective of distributed cognition[J]. Library and information service, 2023, 67(9): 53-62.
5	周霞, 王萍, 陈为东, 等. 政府开放数据用户认知图式联结模型——数据故事视角[J]. 情报资料工作, 2021, 42(4): 64-71.
	ZHOU X, WANG P, CHEN W D, et al. Government open data user cognitive schematic connection model: From the perspective of data story[J]. Information and documentation services, 2021, 42(4): 64-71.
6	曹艳辉. “适度压力型”政民互动: 基于中部省级网络问政平台的数据分析[J]. 新闻与传播评论, 2023, 76(2): 70-81.
	CAO Y H. Political interaction with "appropriate pressure": Large sample data analysis based on the central provincial online political platform[J]. Journalism & communication review, 2023, 76(2): 70-81.
7	贾改革. 政民互动中社会诉求主题挖掘和情感分析——基于人民网领导留言板的数据分析[D]. 杭州: 浙江大学, 2023.
	JIA G G. Social appeal theme mining and emotional analysis in the interaction between government and people - Based on the data analysis of people's daily online leadership message board[D]. Hangzhou: Zhejiang University, 2023.
8	HUBERT R B, ESTEVEZ E, MAGUITMAN A, et al. Analyzing and visualizing government-citizen interactions on twitter to support public policy-making[J]. Digital government: Research and practice, 2020, 1(2): 1-20.
9	BELKAHLA DRISS O, MELLOULI S, TRABELSI Z. From citizens to government policy-makers: Social media data analysis[J]. Government information quarterly, 2019, 36(3): 560-570.
10	孙倬, 赵红, 王宗水. 网络舆情研究进展及其主题关联关系路径分析[J]. 图书情报工作, 2021, 65(7): 143-154.
	SUN Z, ZHAO H, WANG Z S. Analysis on the association and evolution path of Internet public opinion[J]. Library and information service, 2021, 65(7): 143-154.
11	杨柳, 罗文倩, 邓春林, 等. 基于灰色关联分析的舆情分级与预警模型研究[J]. 情报科学, 2020, 38(8): 28-34.
	YANG L, LUO W Q, DENG C L, et al. Classification and early warning model of public opinion based on grey correlation analysis[J]. Information science, 2020, 38(8): 28-34.
12	武慧娟, 张海涛, 王尽晖, 等. 基于熵权法的网络舆情预警模糊综合评价模型研究[J]. 情报科学, 2018, 36(7): 58-61.
	WU H J, ZHANG H T, WANG J H, et al. Research on the fuzzy comprehensive evaluation model of online public opinion pre-warning based on entropy weight method[J]. Information science, 2018, 36(7): 58-61.
13	彭程, 祁凯, 黎冰雪. 基于SIR-EGM模型的复杂网络舆情传播与预警机制研究[J]. 情报科学, 2020, 38(3): 145-153.
	PENG C, QI K, LI B X. Communication and early warning mechanism of public opinion in complex networks based on SIR-EGM model[J]. Information science, 2020, 38(3): 145-153.
14	李知谕, 杨柳, 邓春林. 基于弹幕与评论情感倾向的食品安全舆情预警研究[J]. 科技情报研究, 2022, 4(3): 33-45.
	LI Z Y, YANG L, DENG C L. Public opinion early warning on food security based on sentiment of video bullet screen and comments[J]. Scientific information research, 2022, 4(3): 33-45.
15	GROUP E B M W. Evidence-based medicine. A new approach to teaching the practice of medicine[J]. JAMA, 1992, 268(17): 2420-2425.
16	刘瑞, 马海群. 基于循证决策的开放数据政策制定体系构建[J]. 现代情报, 2020, 40(8): 129-133.
	LIU R, MA H Q. Construction of open data policy making system based on evidence-based decision making[J]. Journal of modern information, 2020, 40(8): 129-133.
17	马小亮, 樊春良. 基于证据的政策: 思想起源、发展和启示[J]. 科学学研究, 2015, 33(3): 353-362.
	MA X L, FAN C L. The origin and development of "evidence-based policy"[J]. Studies in science of science, 2015, 33(3): 353-362.
18	NAM T, PARDO T A. Understanding municipal service integration: An exploratory study of 311 contact centers[J]. Journal of urban technology, 2014, 21(1): 57-78.
19	DAVIES P. What is evidence-based education?[J]. British journal of educational studies, 1999, 47(2): 108-121.
20	ROUSSEAU D M. Making evidence-based organizational decisions in an uncertain world[J]. Organizational dynamics, 2018, 47(3): 135-146.
21	魏景容. 大数据时代循证决策研究: 一个分析框架[J]. 中国科技论坛, 2020(7): 24-32.
	WEI J R. Research on evidence-based decision making in the era of big data: An analytical framework[J]. Forum on science and technology in China, 2020(7): 24-32.
22	叶艳, 吴鹏. 循证决策视角下的患者健康咨询主题分析[J]. 情报理论与实践, 2022, 45(2): 198-203, 190.
	YE Y, WU P. Topic analysis of patients' health consultation based on evidence-based decision-making theory[J]. Information studies: Theory & application, 2022, 45(2): 198-203, 190.
23	何玉仙. 大数据时代政府循证决策模式探究[J]. 信息系统工程, 2023(8): 120-123.
	HE Y X. Research on evidence-based decision-making model of government in the era of big data[J]. China CIO news, 2023(8): 120-123.
24	CHEVALIER J A, MAYZLIN D. The effect of word of mouth on sales: Online book reviews[J]. Journal of marketing research, 2006, 43(3): 345-354.
25	范逢春. 国家治理现代化场域中的社会治理话语体系重构——基于话语分析的基本框架[J]. 行政论坛, 2018, 24(6): 109-115.
	FAN F C. Reconstruction of social governance discourse system in the field of modernizing the state governance: Based on general framework of discourse analysis[J]. Administrative tribune, 2018, 24(6): 109-115.
26	王瑶. 网络问政平台中公众诉求表达对政府回应的影响研究[D]. 成都: 电子科技大学, 2024.
	WANG Y. Research on the influence of public appeal expression on the government response in the online politics platform[D]. Chengdu: University of Electronic Science and Technology of China, 2024.
27	赵国洪, 刘伟章. 我国政府网站政民互动模型及实证分析[J]. 情报杂志, 2012, 31(2): 195-202.
	ZHAO G H, LIU W Z. Construction and empirical analysis of interactivity model for government websites in China[J]. Journal of intelligence, 2012, 31(2): 195-202.
28	詹承豫. 中国城市风险沟通决策的影响因素研究[J]. 治理研究, 2019, 35(5): 13-21.
	ZHAN C Y. Research on the influencing factors of communication and decision about urban risk[J]. Governance studies, 2019, 35(5): 13-21.
29	邱文. 公众诉求事件关键数据的空间智能提取与分析[J]. 城市勘测, 2020(2): 27-30.
	QIU W. Spatial intelligence extraction and analysis of key data of public appeal events[J]. Urban geotechnical investigation & surveying, 2020(2): 27-30.
30	张楠迪扬, 郑旭扬, 赵乾翔. 政府回应性: 作为日常治理的“全回应”模式——基于LDA主题建模的地方政务服务“接诉即办”实证分析[J]. 中国行政管理, 2023(3): 68-78.
	ZHANG N, ZHENG X Y, ZHAO Q X. Government responsiveness: An all-response' pattern of daily governance - An empirical study of local government hotline appeals using latent dirichlet allocation(LDA)[J]. Chinese public administration, 2023(3): 68-78.
31	王东琪. 网络问政的舆情挖掘及引导研究——以河北省“领导留言板”为例[D]. 石家庄: 河北经贸大学, 2023.
	WANG D Q. Research on public opinion mining and guidance of online politics - A case study of "leader message board" in Hebei Province[D]. Shijiazhuang: Hebei University of Economics and Business, 2023.
32	梅潇, 查先进, 严亚兰. 智能推荐环境下移动社交媒体用户隐私风险感知影响机理研究[J]. 情报理论与实践, 2024, 47(1): 57-64.
	MEI X, ZHA X J, YAN Y L. Influencing mechanism of privacy risk perception in the context of mobile social media intelligent recommendation[J]. Information studies: Theory & application, 2024, 47(1): 57-64.
33	SAZZED S. BengSentiLex and BengSwearLex: Creating lexicons for sentiment analysis and profanity detection in low-resource Bengali language[J]. PeerJ computer science, 2021, 7: e681
34	曾子明, 孙守强, 李青青. 基于融合策略的突发公共卫生事件网络舆情多模态负面情感识别[J]. 情报学报, 2023, 42(5): 611-622.
	ZENG Z M, SUN S Q, LI Q Q. Multimodal negative sentiment recognition in online public opinion during public health emergencies based on fusion strategy[J]. Journal of the China society for scientific and technical information, 2023, 42(5): 611-622.
35	臧雷振, 王栋, 仉佳璐. 社会科学研究中的敏感议题: 特征判断与应对方法[J]. 学习与探索, 2023(4): 36-42, 186.
	ZANG L Z, WANG D, ZHANG J L. Sensitive issues in social science research: Judging features and handling methods[J]. Study & exploration, 2023(4): 36-42, 186.
36	王磊, 高茂庭. 基于CRITIC权与灰色关联的隐写分析算法综合评估[J]. 计算机工程, 2017, 43(4): 154-159.
	WANG L, GAO M T. Comprehensive evaluation of steganography analysis algorithm based on CRITIC weight and grey relation[J]. Computer engineering, 2017, 43(4): 154-159.
37	夏立新, 杨元, 周鼎. “双一流”建设视阈下我国高校文献资源保障水平评价指标体系构建研究[J]. 图书情报工作, 2022, 66(7): 57-65.
	XIA L X, YANG Y, ZHOU D. Research on the construction of the evaluation index system of the document resource guarantee level in Chinese universities in "double first-class" construction view[J]. Library and information service, 2022, 66(7): 57-65.
38	苏州市行政审批局. “苏州12345”2023年度工作情况发布[EB/OL]. [2024-05-12].
39	苏州市人民政府. 2024年政府工作报告[EB/OL]. [2024-05-12].

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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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