Topic Resonance of Micro-blogs on Similar Public Health Emergencies

doi:10.11925/infotech.2096-3467.2019.0732

Data Analysis and Knowledge Discovery

2020, Vol. 4

Issue (2/3): 122-133 DOI: 10.11925/infotech.2096-3467.2019.0732

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Topic Resonance of Micro-blogs on Similar Public Health Emergencies

Liang Yanping¹,An Lu^1,²(

),Liu Jing²

¹Center for Studies of Information Resources, Wuhan University, Wuhan 430072, China
²School of Information Management, Wuhan University, Wuhan 430072, China

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Abstract

[Objective] This study aims to explore the resonance patterns of micro-blog users on different topics from similar public health emergencies.[Methods] We constrcted a random resonance model for sub-topics of public health emergencies based on the Langevin equation and collected more than 170,000 microblog entries on the Shandong vaccine incident and the Changchun Changsheng vaccine incident from the Sina Weibo platform. We analyzed the resonance pattern of micro-blog topics by calculating topic factors, geography factors, attitude values and topic salience.[Results] The topics about the progress of events, the public opinion, and the government response generated obvious resonance. However, the topics on the background knowledge and post-measures failed to cause resonance from similar public health emergencies.[Limitations] We only analyzed the resonance patterns with micro-blogging topics on two similar events. More research is needed to examine our findings with other cases.[Conclusions] Resonances exist between the topics of similar public health emergencies, which are related to the number of relevant micro-blog entries, topic contents and other factors.

Key words： Microblogging Topics Stochastic Resonance Langevin Equation Online Public Opinions Emergency Similar Events

Received: 21 June 2019 Published: 26 April 2020

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Corresponding Authors: Lu An E-mail: anlu97@163.com

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Cite this article:

Liang Yanping,An Lu,Liu Jing. Topic Resonance of Micro-blogs on Similar Public Health Emergencies. Data Analysis and Knowledge Discovery, 2020, 4(2/3): 122-133.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.0732 OR https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2020/V4/I2/3/122

情感值 $e$	情感强度 $e ?$
$e$ <-10000000000	0.00
-1000000000≤ $e$ <-100000000	0.05
-100000000≤ $e$ <-10000000	0.10
…	…
-100≤ $e$ <-10	0.45
-10≤ $e$ <0	0.50
0≤ $e$ ≤10	0.50
10<e≤100	0.55
…	…
1000000000< $e$ ≤10000000000	0.95
10000000000< $e$	1.00

Emotional Intensity of Microblogging Posts

话题因素 $i$	地域因素 $r$	态度值 $p$	长生疫苗事件热度 $H$	山东疫苗事件舆情指数 $s$
0.03	0.06	0.51	0.61	0.39

Parameters of the Vaccine Event Resonance Model

Stochastic Resonance in Two Vaccine Events

Topic Types

Popular Weibo Posts Distribution of Different Topic Types

Distribution of Geographical Factors and Topic Factors

话题类型	事件进展	群众意见	政府回应	知识科普	事后措施
$p$	0.51	0.49	0.51	0.52	0.51

Topic Attitude Values of Microblogging Users

话题类型	长春疫苗事件 $s 1$	山东疫苗事件 $s 2$
事件进展	0.33	0.26
群众意见	0.48	0.50
政府回应	0.10	0.16
知识科普	0.06	0.05
事后措施	0.03	0.03

The Salience of Topics in Two Vaccine Events

话题因素 $i$	地域因素 $r$	态度值 $p$	长生疫苗话题热度 $s 1$	山东疫苗话题热度 $s 2$
0.05	0.03	0.51	0.33	0.26

Resonance Model Parameters of Event Progress Topics

Resonance of Event Progress Topic

话题因素 $i$	地域因素 $r$	态度值 $p$	长生疫苗话题热度 $s 1$	山东疫苗话题热度 $s 2$
0.17	0.21	0.49	0.48	0.50

Resonance Model Parameters of Public Opinion Topics

Resonance of Public Opinion Topics

话题因素 $i$	地域因素 $r$	态度值 $p$	长生疫苗话题热度 $s 1$	山东疫苗话题热度 $s 2$
0.10	0.06	0.51	0.10	0.16

Resonance Model Parameters of Government Response Topics

Resonance of Government Response Topics

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