Please wait a minute...
Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (4): 42-52    DOI: 10.11925/infotech.2096-3467.2018.1061
Current Issue | Archive | Adv Search |
Public Opinion Propagation and Evolution of Public Health Emergencies in Social Media Era: A Case Study of 2018 Vaccine Event
Lin Wang1,2,Ke Wang1,Jiang Wu1,2()
1School of Information Management, Wuhan University, Wuhan 430072, China
2Center for E-commerce Research and Development, Wuhan University, Wuhan 430072, China
Download: PDF (697 KB)   HTML ( 10
Export: BibTeX | EndNote (RIS)      

[Objective] This paper tries to investigate the rules of public opinion propagation and evolution of public health emergencies and propose corresponding policies in the context of social media era. [Methods] Based on ELM, TAM and life cycle theory, the influencing factor model was established to explore the impact of information publishers, information content and information release time on public opinion propagation of public health emergencies. [Results] The empirical analysis showed that the right to discourse is mastered by different interest groups in different periods of public opinion development. Information with great timeliness and novelty spreads more widely. If official media show some subjectivity, the retweet volume of their tweets will be larger. [Limitations] Only one case was empirically analyzed and the compatibility of the model needs to be improved. [Conclusions] The model that comprehensively considers identity of information source, quality of information content and life cycle is a good way to explain the public opinion propagation and evolution rules of public health emergencies on social media platforms.

Key wordsSocial Media      Public Health Emergencies      Public Opinion Propagation      Public Opinion Evolution     
Received: 22 September 2018      Published: 29 May 2019

Cite this article:

Lin Wang,Ke Wang,Jiang Wu. Public Opinion Propagation and Evolution of Public Health Emergencies in Social Media Era: A Case Study of 2018 Vaccine Event. Data Analysis and Knowledge Discovery, 2019, 3(4): 42-52.

URL:     OR

[1] 朱江丽. 新媒体推动公民参与社会治理: 现状、问题与对策[J]. 中国行政管理, 2017(6): 49-53.
[1] (Zhu Jiangli.New Media Boosting Citizen Participation in Society Governance: Mechanism, Problems and Solutions[J]. Chinese Public Administration, 2017(6): 49-53.)
[2] 郭春侠, 刘惠, 储节旺. 新媒体环境下网络舆情治理大数据能力建设研究[J]. 情报理论与实践, 2018, 41(12): 46-54.
[2] (Guo Chunxia, Liu Hui, Chu Jiewang.Big Data Capability Construction of Network Public Opinion Governance Under the New Media Environment[J]. Information Studies: Theory & Application, 2018, 41(12): 46-54.)
[3] Folayan M O, Haire B.Communitarian Societies and Public Engagement in Public Health[J]. Critical Public Health, 2017, 27(1): 6-13.
[4] 中华人民共和国国务院. 突发公共卫生事件应急条例[M]. 北京: 中国方正出版社, 2003.
[4] (The State Council of PRC. Emergency Regulations for Public Health Emergencies[M]. Beijing: China Fangzheng Press, 2003.)
[5] Lotstein D, Seid M, Ricci K, et al.Using Quality Improvement Methods to Improve Public Health Emergency Preparedness: PREPARE for Pandemic Influenza[J]. Health Affairs, 2008, 27(5): w328-w339.
[6] 索继江, 邢玉斌, 田晓丽, 等. 医院感染管理科在应对突发公共卫生事件中的作用[J]. 解放军医院管理杂志, 2004, 11(6): 532-533.
[6] (Suo Jijiang, Xing Yubin, Tian Xiaoli, et al.Hospital Infection Management and Its Role in Preventing and Controlling Emergency Public Health Event[J]. Hospital Administration Journal of Chinese People's Liberation Army, 2004, 11(6): 532-533.)
[7] 常玲慧, 马斌. 突发公共卫生事件应急决策中的知识管理研究[J]. 科技管理研究, 2013(4): 203-207.
[7] (Chang Linghui, Ma Bin.Research on Knowledge Management in Public Health Emergency Decision-making[J]. Science and Technology Management Research, 2013(4): 203-207.)
[8] Rose D A, Murthy S, Brooks J, et al.The Evolution of Public Health Emergency Management as a Field of Practice[J]. American Journal of Public Health, 2017, 107(S2): S126-S133.
[9] 陈海平, 郝艳华, 吴群红, 等. 突发公共卫生事件影响综合评价指标体系构建[J]. 中国公共卫生, 2013, 29(5): 628-631.
[9] (Chen Haiping, Hao Yanhua, Wu Qunhong, et al.Establishment of Comprehensive Evaluation Index System for Impact Assessment of Public Health Emergency[J]. Chinese Journal of Public Health, 2013, 29(5): 628-631.)
[10] 李燕凌, 丁莹. 网络舆情公共危机治理中社会信任修复研究——基于动物疫情危机演化博弈的实证分析[J]. 公共管理学报, 2017, 14(4): 91-101, 157.
[10] (Li Yanling, Ding Ying.Research on Social Trust Repair of Public Crisis Governance Under Network Public Opinion——Based on an Empirical Analysis of Evolutionary Game of Animal Epidemic Public Crisis[J]. Journal of Public Management, 2017, 14(4): 91-101, 157.)
[11] 安璐, 杜廷尧, 李纲, 等. 突发公共卫生事件利益相关者在社交媒体中的关注点及演化模式[J]. 情报学报, 2018, 37(4): 394-405.
[11] (An Lu, Du Tingyao, Li Gang, et al.Concerns and Evolutionary Patterns of Stakeholders on Social Media Platforms During Public Health Emergencies[J]. Journal of the China Society for Scientific and Technical Information, 2018, 37(4): 394-405.)
[12] 安璐, 欧孟花. 突发公共卫生事件利益相关者的社会网络情感图谱研究[J]. 图书情报工作, 2017, 61(20): 120-130.
[12] (An Lu, Ou Menghua.Social Network Sentiment Map of the Stakeholders in Public Health Emergencies[J]. Library and Information Service, 2017, 61(20): 120-130.)
[13] 安璐, 易兴悦, 余传明, 等. 突发公共卫生事件微博影响力的预测研究[J]. 情报理论与实践, 2017, 40(8): 76-81, 42.
[13] (An Lu, Yi Xingyue, Yu Chuanming, et al.Prediction of the Influence of Public Health Emergencies Micro-blog[J]. Information Studies: Theory & Application, 2017, 40(8): 76-81, 42.)
[14] 张敏, 夏宇, 刘晓彤. 重大医疗伤害事件网络舆情能量传播过程分析——以“魏则西事件”为例[J]. 情报杂志, 2016, 35(12): 58-62, 74.
[14] (Zhang Min, Xia Yu, Liu Xiaotong.Network Public Opinion Energy Transmission Process Analysis of Major Medical Damage Event: “Wei Zexi” Event as an Example[J]. Journal of Intelligence, 2016, 35(12): 58-62, 74.)
[15] 廖海涵, 王曰芬. 社交媒体舆情信息传播效果影响因素研究——以新浪微博“8.12天津爆炸”事件为例[J]. 现代图书情报技术, 2016(12): 85-93.
[15] (Liao Haihan, Wang Yuefen.Public Opinion Dissemination over Social Media: Case Study of Sina Weibo and “8.12 Tianjin Explosion”[J]. New Technology of Library and Information Service, 2016(12): 85-93.)
[16] Yoo E, Rand W, Eftekhar M, et al.Evaluating Information Diffusion Speed and Its Determinants in Social Media Networks During Humanitarian Crises[J]. Journal of Operations Management, 2016, 45: 123-133.
[17] 张玥, 孙霄凌, 朱庆华. 基于ELM模型的微博舆情传播影响因素研究——以新浪微博为例[J]. 情报学报, 2014, 33(4):426-438.
[17] (Zhang Yue, Sun Xiaoling, Zhu Qinghua.Research on Factors that Influence Microblog Opinion Communication Based on Elaboration Likelihood Model: Taking Sina Microblog as an Example[J]. Journal of the China Society for Scientific and Technical Information, 2014, 33(4): 426-438.)
[18] 赵丹, 王晰巍, 相甍甍, 等. 新媒体环境下的微博舆情传播态势模型构建研究——基于信息生态视角[J]. 情报杂志, 2016, 35(10): 173-180.
[18] (Zhao Dan, Wang Xiwei, Xiang Mengmeng, et al.Model of Micro-blog Public Opinion Dissemination Trend Under the New Media Environment: An Information Ecology Perspective[J]. Journal of Intelligence, 2016, 35(10): 173-180.)
[19] 李彪. 网络事件传播阶段及阈值研究——以2010年34个热点网络舆情事件为例[J]. 国际新闻界, 2011, 33(10): 22-27.
[19] (Li Biao.Communication Phase and Its Threshold of Network Events: 34 Network Events in 2010 as an Example[J]. Chinese Journal of Journalism & Communication, 2011, 33(10): 22-27.)
[20] 李明德, 蒙胜军, 张宏邦. 微博舆情传播模式研究——基于过程的分析[J]. 情报杂志, 2014, 33(2): 120-127.
[20] (Li Mingde, Meng Shengjun, Zhang Hongbang.Communication Mode of Microblog: A Study Based on the Process Analysis[J]. Journal of Intelligence, 2014, 33(2): 120-127.)
[21] 晏敬东, 杨彩霞, 张炜南. 基于生命周期理论的微博舆情引控研究[J]. 情报杂志, 2017, 36(8): 88-93,75.
[21] (Yan Jingdong, Yang Caixia, Zhang Weinan.Study on the Control and Guidance of Micro-Blog Public Opinion Based on the Lifecycle Theory[J]. Journal of Intelligence, 2017, 36(8): 88-93, 75.)
[22] Petty R E, Cacioppo J T.The Elaboration Likelihood Model of Persuasion[J]. Advances in Experimental Social Psychology, 1986, 19: 123-205.
[23] Davis F D, Bagozzi R P, Warshaw P R.User Acceptance of Computer Technology: A Comparison of Two Theoretical Models[J]. Management Science, 1989, 35(8): 982-1003.
[24] Sussman S W, Siegal W S.Informational Influence in Organizations: An Integrated Approach to Knowledge Adoption[J]. Information Systems Research, 2003, 14(1): 47-65.
[25] Rauniar R, Rawski G, Yang J, et al.Technology Acceptance Model (TAM) and Social Media Usage: An Empirical Study on Facebook[J]. Journal of Enterprise Information Management, 2014, 27(1): 6-30.
[26] Liu H L, Li Y. Weibo Information Propagation Dissemination Based on User Behavior Using ELM[J]. Mathematical Problems in Engineering, 2015: Article No. 876218.
[27] 江耘, 严由卫. 社会化媒体信息传播行为影响因素研究——以“网络红人”为视角[J]. 情报资料工作, 2017(5): 70-75.
[27] (Jiang Yun, Yan Youwei.A Probe into the Influencing Factors of Social Media Information Dissemination Behavior: From the Perspective of Net Celebrity[J]. Information and Documentation Services, 2017(5): 70-75.)
[28] 刘嘉琪, 齐佳音, 朱舸. 社交媒体中企业生成内容(EGC)的社会化传播行为研究——基于内容和情感分析视角[J]. 情报科学, 2018, 36(8): 135-141.
[28] (Liu Jiaqi, Qi Jiayin, Zhu Ge.Research on User Social Communication Behavior of EGC in Social Media: Based on Content and Sentiment Analysis Perspective[J]. Information Science, 2018, 36(8): 135-141.)
[29] Lotan G, Graeff E, Ananny M, et al.The Revolutions were Tweeted: Information Flows During the 2011 Tunisian and Egyptian Revolutions[J]. International Journal of Communication, 2011, 5: 1375-1405.
[30] Li R, Suh A.Factors Influencing Information Credibility on Social Media Platforms: Evidence from Facebook Pages[J]. Procedia Computer Science, 2015, 72: 314-328.
[31] Li L, Zhang Q, Tian J, et al.Characterizing Information Propagation Patterns in Emergencies: A Case Study with Yiliang Earthquake[J]. International Journal of Information Management, 2018, 38(1): 34-41.
[32] 张玥, 孙霄凌, 浦正宁, 等. 微博舆情传播影响因素研究——基于信源特征和信息形式的视角[J]. 情报资料工作, 2014(3): 59-64.
[32] (Zhang Yue, Sun Xiaoling, Pu Zhengning, et al.Influencing Factors of Microblog Public Opinion Dissemination: on the Perspective of Information Source Characteristic and Information Form[J]. Information and Documentation Services, 2014(3): 59-64.)
[33] 李晓静. 社交媒体用户的信息加工与信任判断——基于眼动追踪的实验研究[J]. 新闻与传播研究, 2017, 24(10): 49-67, 127-128.
[33] (Li Xiaojing. Social Media User's Information Processing and Credibility Judgement: An Eye-Tracking Experiment[J]. Journalism & Communication, 2017, 24(10): 49-67, 127-128.)
[34] 金晓玲, 冯慧慧, 周中允. 微信朋友圈中健康信息传播行为研究[J]. 管理科学, 2017, 30(1): 73-82.
[34] (Jin Xiaoling, Feng Huihui, Zhou Zhongyun.An Empirical Study on Healthcare Information Diffusion Behavior in WeChat Moments[J]. Journal of Management Science, 2017, 30(1): 73-82.)
[35] Sylvie G.Book Review: The Elements of Journalism: What Newspeople Should Know and the Public Should Expect[J]. Journalism & Mass Communication Quarterly, 2001, 78(4): 851-853.
[36] Huffaker D.Dimensions of Leadership and Social Influence in Online Communities[J]. Human Communication Research, 2010, 36(4): 593-617.
[37] Chen R, Sakamoto Y.Feelings and Perspective Matter: Sharing of Crisis Information in Social Media[C]// Proceedings of the 47th Hawaii International Conference on System Science. IEEE, 2014: 1958-1967.
[38] Ferrara E, Yang Z. Quantifying the Effect of Sentiment on Information Diffusion in Social Media[J/OL]. PeerJ Computer Science, 2015, 1: e26. https://doi.org110.7717/peerj-cs.26
[39] Berger J, Milkman K L.What Makes Online Content Viral?[J]. Journal of Marketing Research, 2012, 49(2): 192-205.
[40] Stieglitz S, Dang-Xuan L.Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior[J]. Journal of Management Information Systems, 2013, 29(4): 217-248.
[41] Zhao K, Kumar A.Who Blogs What: Understanding the Publishing Behavior of Bloggers[J]. World Wide Web: Internet and Web Information Systems, 2013, 16(5-6): 621-644.
[42] Chang H H, Chuang S S.Social Capital and Individual Motivations on Knowledge Sharing: Participant Involvement as a Moderator[J]. Information & Management, 2011, 48(1): 9-18.
[43] Marwick A, Boyd D.To See and be Seen: Celebrity Practice on Twitter[J]. Convergence, 2011, 17(2): 139-158.
[44] Pervin N, Takeda H, Toriumi F.Factors Affecting Retweetability: An Event-Centric Analysis on Twitter[C]//Proceedings of the 35th International Conference on Information Systems. 2014.
[45] 方洁, 龚立群. 利益相关者视角下的微博舆情监测指标体系研究[J]. 情报杂志, 2013, 32(9): 29-33.
[45] (Fang Jie, Gong Liqun.A Study on Microblog Public Opinion Monitoring Indexes from Perspective of Stakeholders[J]. Journal of Intelligence, 2013, 32(9): 29-33.)
[46] Remy C, Pervin N, Toriumi F, et al.Information Diffusion on Twitter: Everyone Has Its Chance, but All Chances are Not Equal[C]//Proceedings of the 9th International Conference on Signal-image Technology & Internet-based Systems, Kyoto, Japan. IEEE, 2014.
[47] 卢永春. “让违法者倾家荡产”成为网民的高频词[EB/OL]. (2018-07-27). [2018-08-30]. 2018/0727/c209043-30174707.html
[47] (Lu Yongchun. “Make the Lawbreakers Breakup” Has Become High-Frequency Words in Public[EB/OL]. (2018-07-27). [2018-08-30].
[48] Gao R, Hao B, Li H, et al.Developing Simplified Chinese Psychological Linguistic Analysis Dictionary for Microblog[C]// Proceedings of the International Conference on Brain and Health Informatics. Springer, 2013, 8211: 359-368.
[49] Mikolov T, Chen K, Corrado G, et al. Efficient Estimation of Word Representations in Vector Space[OL]. arXiv Preprint, arXiv:1301.3781, 2013.
[50] 龙强, 李艳红. 从宣传到霸权: 社交媒体时代“新党媒”的传播模式[J]. 国际新闻界, 2017, 39(2): 52-65.
[50] (Long Qiang, Li Yanhong.From Propaganda to Hegemony: Communication Models of New Party Media in the Context of Social Media[J]. Chinese Journal of Journalism & Communication, 2017, 39(2): 52-65.)
[1] Xie Hao,Mao Jin,Li Gang. Sentiment Classification of Image-Text Information with Multi-Layer Semantic Fusion[J]. 数据分析与知识发现, 2021, 5(6): 103-114.
[2] Ma Yingxue,Zhao Jichang. Patterns and Evolution of Public Opinion on Weibo During Natural Disasters: Case Study of Typhoons and Rainstorms[J]. 数据分析与知识发现, 2021, 5(6): 66-79.
[3] Zhang Guobiao,Li Jie. Detecting Social Media Fake News with Semantic Consistency Between Multi-model Contents[J]. 数据分析与知识发现, 2021, 5(5): 21-29.
[4] Han Pu, Zhang Wei, Zhang Zhanpeng, Wang Yuxin, Fang Haoyu. Sentiment Analysis of Weibo Posts on Public Health Emergency with Feature Fusion and Multi-Channel[J]. 数据分析与知识发现, 2021, 5(11): 68-79.
[5] Liu Qian, Li Chenliang. A Survey of Topic Evolution on Social Media[J]. 数据分析与知识发现, 2020, 4(8): 1-14.
[6] Li Gang, Guan Weidong, Ma Yaxue, Mao Jin. Predicting Social Media Visibility of Scholarly Articles[J]. 数据分析与知识发现, 2020, 4(8): 63-74.
[7] Ying Tan,Jin Zhang,Lixin Xia. A Survey of Sentiment Analysis on Social Media[J]. 数据分析与知识发现, 2020, 4(1): 1-11.
[8] Xiwei Wang,Duo Wang,Qingxiao Zheng,Ya’nan Wei. Information Interaction Between User and Enterprise in Online Brand Community: A Study of Virtual Reality Industry[J]. 数据分析与知识发现, 2019, 3(3): 83-94.
[9] Xiaoxiao Zhu,Zunqi Yang,Jing Liu. Construction of an Adverse Drug Reaction Extraction Model Based on Bi-LSTM and CRF[J]. 数据分析与知识发现, 2019, 3(2): 90-97.
[10] Cuiqing Jiang,Yibo Guo,Yao Liu. Constructing a Domain Sentiment Lexicon Based on Chinese Social Media Text[J]. 数据分析与知识发现, 2019, 3(2): 98-107.
[11] Gang Li,Sijing Chen,Jin Mao,Yansong Gu. Spatio-Temporal Comparison of Microblog Trending Topics on Natural Disasters[J]. 数据分析与知识发现, 2019, 3(11): 1-15.
[12] Li Lei,He Daqing,Zhang Chengzhi. Survey on Social Question and Answer[J]. 数据分析与知识发现, 2018, 2(7): 1-12.
[13] Jing Dong,Zhang Dayong. Assessing Trust-Based Users’ Influence in Social Media[J]. 数据分析与知识发现, 2018, 2(7): 26-33.
[14] Li Baozhen,Wang Ya,Zhou Ke. Measuring Credibility of Social Media Contents Based on Bayesian Theory[J]. 数据分析与知识发现, 2017, 1(6): 83-92.
[15] Li Dan. Improving Library Services with the Help of WeChat[J]. 现代图书情报技术, 2016, 32(4): 104-110.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938