Analyzing the spatiotemporal coupling relationship between public opinion and the epidemic during COVID-19
| Date | 22 June 2023 |
| Pages | 1880-1904 |
| DOI | https://doi.org/10.1108/LHT-10-2022-0462 |
| Published date | 22 June 2023 |
| Subject Matter | Library & information science,Librarianship/library management,Library technology,Information behaviour & retrieval,Information user studies,Metadata,Information & knowledge management,Information & communications technology,Internet |
| Author | Jingjing Sun,Ziming Zeng,Tingting Li,Shouqiang Sun |
Analyzing the spatiotemporal
coupling relationship between
public opinion and the epidemic
during COVID-19
Jingjing Sun
School of Information Management, Wuhan University, Wuhan, China
Ziming Zeng
Wuhan University, Wuhan, China, and
Tingting Li and Shouqiang Sun
School of Information Management, Wuhan University, Wuhan, China
Abstract
Purpose –The outbreak of COVID-19 has become a major public health emergency worldwide. How to
effectively guide publicopinion and implement precise prevention and control is a hot topic in current research.
Mining the spatiotemporal coupling between online public opinion and offline epidemics can provide decision
support for the precise management and control of future emergencies.
Design/methodolog y/approach –This study f ocuses on analyzing t he spatiotemporal co upling
relationship between public opinion and the epidemic. First, based on Weibo information and confirmed
case information, a field framework is constructed using field theory. Second, SnowNLP is used for sentiment
mining and LDA is utilized for topic extraction to analyze the topic evolution and the sentiment evolution of
public opinion in each coupling stage. Finally, the spatial model is used to explore the coupling relationship
between public opinion and the epidemic in space.
Findings –The findings show that there is a certain coupling between online public opinion sentiment and
offline epidemics, with a significant coupling relationship in the time dimension, while there is no remarkable
coupling relationship in space. In addition, the core topics of public concern are different at different coupling
stages.
Originality/value –This study deeply explores the spatiotemporal coupling relationship between online
public opinion and offline epidemics, adding a new research perspective to related research. The result can help
the government and relevant departments understand the dynamic development of epidemic events and
achieve precise control while mastering the dynamics of online public opinion.
Keywords COVID-19, Sentiment analysis, Spatiotemporal evolution, Topic mining, Coupling relationship
Paper type Research paper
1. Introduction
With the continuous outbreak of COVID-19, how to effectively guide public opinion,and
implement efficient epidemic prevention measures and emergency management is the
focus of government departments around the world, and it is also a hot spot of current
academic research. In the existing studies related to major health emergencies, most
researchers only focus on online public opinion, mainly including emotional evolution
(Feng et al., 2022), topic evolution (Jian et al., 2018), event extraction (Li et al., 2020), etc.,
ignoring the connection between online public opinion and offline epidemics. However,
mining the coupling relationship between online public opinion and offline epidemics can
greatly help relevant departments to achieve precise prevention and control, and carry out
linkage governance that combines online and offline. Only a few scholars have noticed the
LHT
42,6
1880
This research was supported by: the National Social Science Foundation of China (Grant # 21BTQ046).
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0737-8831.htm
Received 29 September 2022
Revised 4 April 2023
8 May 2023
4 June 2023
Accepted 5 June 2023
Library Hi Tech
Vol. 42 No. 6, 2024
pp. 1880-1904
© Emerald Publishing Limited
0737-8831
DOI 10.1108/LHT-10-2022-0462
importance of the coupling relationship in the study of public health emergencies. Yi and
Chiu (2023) analyzed the public sentiment related to COVID-19 vaccines in Twitter
messages to explore the impact of tweets in digital social circles. Yari Eili and Rezaeenour
(2022) were biased toward studying offline governance, using process mining techniques
to model and analyze the quarantine implementation process. Zhang et al. (2020b)
conducted in-depth research on the spread of the COVID-19 outbreak and constructed a
geographic spread model based on a complex network to quantify the degree of impact of
the epidemic area on surrounding areas. Yuan et al. (2021) visualized the coupling model of
online public opinion and rumors of social hot events and used field theory to reveal the
coupling mechanism of online public opinion and rumors. Ye et al. (2021) used the system
dynamics method to analyze the coupling between online and offline networks caused by
emergencies. At the same time, they utilized online and offline interaction and convergence
as indicators to measure the coupling degree, and finally took the coupling degree as the
standard to analyze the coupling mechanism. In conclusion, no researcher has conducted
an in-depth study on the coupling mechanism between online public opinion and offline
epidemics in health emergencies in the existing studies, which is crucial to the precise
governance of public safety.
Since it is important to study the spatiotemporal coupling relationship between online
public opinion and the offline epidemic for national epidemic management, this paper
divides the temporal stages of public opinion evolution based on the life cycle theory and
constructs a field framework of the public opinion field and physical field from a new
perspective of the field theory (Bourdieu and Wacquant, 1998) as a new perspective.
Subsequently, sentiment analysis and topic mining are conducted on the public opinion
data to explore the coupling relationship between online public opinion and offline
epidemics from the temporal dimension, and to dig deeper into the topic features at
different coupling stages. Finally, the geospatial model is used to investigate the evolution
trend and distribution of epidemic and public opinion and to analyze the coupling
relationship between online public opinion and offline epidemics from both spatial and
temporal dimensions.
2. Related work
2.1 The evolution of public opinion
The coronavirus disease 2019 (COVID-19) has received worldwide attention as a serious
global public health emergency. Studying the evolution of public opinion can help
governments and related departments to guide public opinion and effectively implement
emergency management. In the existing studies, most of the researchers are concerned
with the topic analysis and sentiment evolution as ways to reveal the evolution of public
opinion. At present, the research on topic analysis and topic evolution of public opinion
mainly uses topic models such as LDA (Latent Dirichlet allocation) (Blei et al., 2003)to
extract topics, and then mine the evolution pattern of public opinion topics on this basis.
Scholars used LDA to summarize hot topics in COVID-19 research and revealed related
research trends (Zhu and Lei, 2022;Cao et al., 2022). Chen et al. (2022) used the LDA model
to achieve topic clustering of public opinion and analyzed the topic evolution pattern of
different users according to topic similarity. Zhong (2021) analyzed public opinion
information based on LDA and found that topics of high public interest mainly included
COVID-19 preventive measures, infection symptoms, and community management.
In order to understand the evolutionary trend of public opinion, many scholars have
conducted in-depth studies on the sentiment of public opinion based on sentiment analysis
techniques. Tri Sakti et al. (2021) utilized sentiment analysis methods and supervised
machine learning methods to analyze COVID-19-related posts on media platforms and
Public opinion
and the
epidemic
1881
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