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| The Evolution of the Online Public Opinion of Stakeholders in Emergencies |
| Zhang Jiaomeng1, Shi Rongrong2 |
1.School of Mathematics, Northwest University, Xi’an 710127
2.School of Economics and Management, Northwest University, Xi’an 710127 |
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Abstract In this study, a method for monitoring public opinion on public health emergencies in conjunction with stakeholders is proposed. Taking micro-blog data on the COVID-19 epidemic as an example, 11 types of stakeholder were divided according to their social roles in the epidemic. The models of Latent Dirichlet Allocation (LDA) and LDA2vec were connected in series for topic extraction, and SnowNLP was used for text sentiment classification. Through the statistics of the absolute attention and relative attention of topics, the evolution of the online public opinion of different stakeholders was obtained. The empirical results showed that the stakeholders’ concerns were more consistent during the outbreak period, but more scattered during the stable period, then consistent as the epidemic gradually came under control. Stakeholders in the same roles showed similar topic evolution and emotional evolution, but their focuses were still different. Absolute attention reflects the concerns under the influence of mainstream public opinion, while relative attention reflects the concerns related to the stakeholders’ own interests. The study revealed the evolution of public opinion of stakeholders in public health emergencies, so as to provide a theoretical basis and decision-making references for the government to accurately monitor public opinion trends among different stakeholders in public health emergencies.
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Received: 06 April 2021
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1 刘倩, 李晨亮. 基于社交媒体的话题演变研究综述[J]. 数据分析与知识发现, 2020, 4(8): 1-14.
2 吴晓娟. 基于微博文本的网络舆情主题演化分析——以“蓝色钱江放火案”为例[D]. 南京: 南京大学, 2018: 34-40.
3 Xiong J M, Hswen Y, Naslund J A. Digital surveillance for monitoring environmental health threats: a case study capturing public opinion from Twitter about the 2019 Chennai water crisis[J]. International Journal of Environmental Research and Public Health, 2020, 17(14): 5077.
4 Sadri A M, Hasan S, Ukkusuri S V, et al. Crisis communication patterns in social media during Hurricane Sandy[J]. Transportation Research Record: Journal of the Transportation Research Board, 2018, 2672(1): 125-137.
5 Yuan F X, Li M, Liu R. Understanding the evolutions of public responses using social media: Hurricane Matthew case study[J]. International Journal of Disaster Risk Reduction, 2020, 51: 101798.
6 王艳东, 李昊, 王腾, 等. 基于社交媒体的突发事件应急信息挖掘与分析[J]. 武汉大学学报·信息科学版, 2016, 41(3): 290-297.
7 Cortes C, Vapnik V. Support-vector networks[J]. Machine Learning, 1995, 20(3): 273-297.
8 Wu W J, Li J L, He Z Y, et al. Tracking spatio-temporal variation of geo-tagged topics with social media in China: a case study of 2016 Hefei rainstorm[J]. International Journal of Disaster Risk Reduction, 2020, 50: 101737.
9 赵华, 章成志. 中英文突发事件话题演化对比研究——以H7N9微博为例[J]. 情报资料工作, 2016(3): 19-27.
10 Wang J Z, Zhou Y, Zhang W, et al. Concerns expressed by Chinese social media users during the COVID-19 pandemic: content analysis of Sina Weibo microblogging data[J]. Journal of Medical Internet Research, 2020, 22(11): e22152.
11 刘雅姝, 张海涛, 徐海玲, 等. 多维特征融合的网络舆情突发事件演化话题图谱研究[J]. 情报学报, 2019, 38(8): 798-806.
12 Neppalli V K, Caragea C, Squicciarini A, et al. Sentiment analysis during Hurricane Sandy in emergency response[J]. International Journal of Disaster Risk Reduction, 2017, 21: 213-222.
13 Zhang L, Wei J, Boncella R J. Emotional communication analysis of emergency microblog based on the evolution life cycle of public opinion[J]. Information Discovery and Delivery, 2020, 48(3): 151-163.
14 崔彦琛, 张鹏, 兰月新, 等. 面向时间序列的微博突发事件衍生舆情情感分析研究——以“6.22”杭州保姆纵火案衍生舆情事件为例[J]. 情报科学, 2019, 37(3): 119-126.
15 Huang W D, Wang Q, Cao J. Tracing public opinion propagation and emotional evolution based on public emergencies in social networks[J]. International Journal of Computers Communications & Control, 2018, 13(1): 129-142.
16 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.
17 Blei D M, Ng A Y, Jordan M I. Latent Dirichlet allocation[J]. Journal of Machine Learning Research, 2003, 3: 993-1022.
18 Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space[OL]. (2013-09-07) https://arxiv.org/pdf/1301.3781v3.pdf.
19 Moody C E. Mixing Dirichlet topic models and word embeddings to make lda2vec[OL]. (2016-05-06). https://arxiv.org/pdf/1605.02019v1.pdf.
20 裘惠麟, 邵波. 多源数据环境下科研热点识别方法研究[J]. 图书情报工作, 2020, 64(5): 78-88.
21 李杰, 王雪可, 刘力宾, 等. 医保欺诈事件舆情传播的情感焦点与情感倾向演化研究——基于舆情客体视角[J]. 情报科学, 2020, 38(4): 77-82.
22 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.
23 Maltoni C, Rossi C, Sánchez G. Improving resilience to emergencies through advanced cyber technologies: the I-REACT project[J]. GEOmedia, 2017, 21(3): 18-22.
24 谢科范, 赵湜, 陈刚, 等. 网络舆情突发事件的生命周期原理及集群决策研究[J]. 武汉理工大学学报(社会科学版), 2010, 23(4): 482-486.
25 Fink S. Crisis management: planning for the inevitable[M]. New York: American Management Association, 1986: 20.
26 于晶. 突发事件政府新闻发布的传播效果研究[D]. 上海: 复旦大学, 2010: 48-49.
27 吴亚楠. 传统媒体与自媒体的博弈研究——以突发公共事件报道为例[D]. 哈尔滨: 黑龙江大学, 2012: 29-30.
28 马缘园. 重大突发公共卫生事件中主流媒体与网络舆情有机运动关系探讨——以新冠肺炎疫情信息传播为例[J]. 新闻爱好者, 2020(9): 27-31. |
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