scholarly journals Understanding the Evolution of Government Attention in Response to COVID-19 in China: A Topic Modeling Approach

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 898
Author(s):  
Quan Cheng ◽  
Jianhua Kang ◽  
Minwang Lin
Keyword(s):  
Latent Dirichlet Allocation ◽  
Effective Control ◽  
Public Health Emergencies ◽  
Government Response ◽  
Evolution Path ◽  
Essential Parameter ◽  
Governance Strategies ◽  
Internal Relationships ◽  
Shed Light ◽  
Modelling Approach

The effective control over the outbreak of COVID-19 in China showcases a prompt government response, in which, however, the allocation of attention, as an essential parameter, remains obscure. This study is designed to clarify the evolution of the Chinese government’s attention in tackling the pandemic. To this end, 674 policy documents issued by the State Council of China are collected to establish a text corpus, which is then used to extract policy topics by applying the latent dirichlet allocation (LDA) model, a topic modelling approach. It is found that the response policies take different tracks in a four-stage controlling process, and five policy topics are identified as major government attention areas in all stages. Moreover, a topic evolution path is highlighted to show internal relationships between different policy topics. These findings shed light on the Chinese government’s dynamic response to the pandemic and indicate the strength of applying adaptive governance strategies in coping with public health emergencies.

scholarly journals Sustained Effects of Government Response on the COVID-19 Infection Rate in China: A Multiple Mediation Analysis

2021 ◽  
Vol 18 (23) ◽  
pp. 12422
Author(s):  
Taixiang Duan ◽  
Zhonggen Sun ◽  
Guoqing Shi
Keyword(s):  
Risk Perception ◽  
Infection Rate ◽  
Protective Action ◽  
Intervention Strategy ◽  
Early Response ◽  
Multiple Mediation ◽  
Public Health Emergencies ◽  
Government Response ◽  
Sustained Effects ◽  
The Government

Many scholars have considered the relationship between the government response to COVID-19, an important social intervention strategy, and the COVID-19 infection rate. However, few have examined the sustained impact of an early government response on the COVID-19 infection rate. The current paper fills this gap by investigating a national survey performed in February 2020 and infection data from Chinese cities surveyed 1.5 years after the outbreak of COVID-19. The results suggest that the Chinese government’s early response to COVID-19 significantly and sustainedly reduced China’s COVID-19 infection rate, and that this impact worked through risk perception, the adoption of protective action recommendations (PARs), and the chain-mediating effects of risk perception and the adoption of PARs, respectively. These findings have important practical value. In demonstrating how government response and infection rate at the macro level are connected to the behaviour of individuals at the micro level, they suggest feasible directions for curbing the spread of diseases such as COVID-19. When facing such public health emergencies, the focus should be on increasing the public’s risk perception and adoption of PARs.

scholarly journals An Influence Prediction Model for Microblog Entries on Public Health Emergencies

2019 ◽  
Vol 3 (2) ◽  
pp. 102-115 ◽  
Author(s):  
Lu An ◽  
Xingyue Yi ◽  
Yuxin Han ◽  
Gang Li
Keyword(s):  
Public Health ◽  
Prediction Model ◽  
Latent Dirichlet Allocation ◽  
Prediction Models ◽  
Data Sets ◽  
Allocation Model ◽  
Latent Dirichlet Allocation Model ◽  
Public Health Emergencies ◽  
Proposed Model ◽  
The Individual

Abstract This study aims at constructing a microblog influence prediction model and revealing how the user, time, and content features of microblog entries about public health emergencies affect the influence of microblog entries. Microblog entries about the Ebola outbreak are selected as data sets. The BM25 latent Dirichlet allocation model (LDA-BM25) is used to extract topics from the microblog entries. A microblog influence prediction model is proposed by using the random forest method. Results reveal that the proposed model can predict the influence of microblog entries about public health emergencies with a precision rate reaching 88.8%. The individual features that play a role in the influence of microblog entries, as well as their influence tendencies are also analyzed. The proposed microblog influence prediction model consists of user, time, and content features. It makes up the deficiency that content features are often ignored by other microblog influence prediction models. The roles of the three features in the influence of microblog entries are also discussed.

scholarly journals Smart literature review: a practical topic modelling approach to exploratory literature review

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Claus Boye Asmussen ◽  
Charles Møller
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Topic Modelling ◽  
Learning Methods ◽  
The Past ◽  
Machine Learning Methods ◽  
Literature Reviews ◽  
Modelling Approach

Abstract Manual exploratory literature reviews should be a thing of the past, as technology and development of machine learning methods have matured. The learning curve for using machine learning methods is rapidly declining, enabling new possibilities for all researchers. A framework is presented on how to use topic modelling on a large collection of papers for an exploratory literature review and how that can be used for a full literature review. The aim of the paper is to enable the use of topic modelling for researchers by presenting a step-by-step framework on a case and sharing a code template. The framework consists of three steps; pre-processing, topic modelling, and post-processing, where the topic model Latent Dirichlet Allocation is used. The framework enables huge amounts of papers to be reviewed in a transparent, reliable, faster, and reproducible way.

Governing public health emergencies during the coronavirus disease outbreak: Lessons from four Chinese cities in the first wave

Urban Studies ◽  
2021 ◽  
pp. 004209802110493
Author(s):  
Lingyue Li ◽  
Surong Zhang ◽  
Jinfeng Wang ◽  
Xiaoming Yang ◽  
Lan Wang
Keyword(s):  
Public Health ◽  
Local Governments ◽  
Local Governance ◽  
Local Level ◽  
Effective Control ◽  
Chinese Government ◽  
Effective Response ◽  
Chinese Cities ◽  
Public Health Emergencies ◽  
Policy Measures

The ongoing coronavirus disease (COVID-19) pandemic has had a far-reaching impact on urban living, prompting emergency preparedness and response from public health governance at multiple levels. The Chinese government has adopted a series of policy measures to control infectious disease, for which cities are the key spatial units. This research traces and reports analyses of those policy measures and their evolution in four Chinese cities: Zhengzhou, Hangzhou, Shanghai and Chengdu. The theoretical framework stems from conceptualisations of urban governance and its role in public health emergencies, wherein crisis management and emergency response are highlighted. In all four cities, the trend curves of cumulative diagnosed cases, critical policies launched in key time nodes and local governance approaches in the first wave were identified and compared. The findings suggest that capable local leadership is indispensable for controlling the coronavirus epidemic, yet local governments’ approaches are varied, contributing to dissimilar local epidemic control policy pathways and positive outcomes in the fight against COVID-19. The effectiveness of disease control is determined by how local governments’ measures have adapted to geospatial and socioeconomic heterogeneity. The coordinated actions from central to local governments also reveal an efficient, top-down command transmission and execution system for coping with the pandemic. This article argues that effective control of pandemics requires both a holistic package of governance strategies and locally adaptive governance measures/processes, and concludes with proposals for both a more effective response at the local level and identification of barriers to achieving these responses within diverse subnational institutional contexts.

An Influence Prediction Model for Microblog Entries on Public Health Emergencies

2019 ◽  
Vol 0 (0) ◽  
Author(s):  
Lu An ◽  
Xingyue Yi ◽  
Yuxin Han ◽  
Gang Li
Keyword(s):  
Public Health ◽  
Prediction Model ◽  
Latent Dirichlet Allocation ◽  
Prediction Models ◽  
Data Sets ◽  
Allocation Model ◽  
Latent Dirichlet Allocation Model ◽  
Public Health Emergencies ◽  
Proposed Model ◽  
The Individual

Abstract This study aims at constructing a microblog influence prediction model and revealing how the user, time, and content features of microblog entries about public health emergencies affect the influence of microblog entries. Microblog entries about the Ebola outbreak are selected as data sets. The BM25 latent Dirichlet allocation model (LDA-BM25) is used to extract topics from the microblog entries. A microblog influence prediction model is proposed by using the random forest method. Results reveal that the proposed model can predict the influence of microblog entries about public health emergencies with a precision rate reaching 88.8%. The individual features that play a role in the influence of microblog entries, as well as their influence tendencies are also analyzed. The proposed microblog influence prediction model consists of user, time, and content features. It makes up the deficiency that content features are often ignored by other microblog influence prediction models. The roles of the three features in the influence of microblog entries are also discussed.

scholarly journals Discovering Trends of Mobile Learning Research Using Topic Modelling Approach

2020 ◽  
Vol 14 (09) ◽  
pp. 4
Author(s):  
Almed Hamzah ◽  
Ahmad Fathan Hidayatullah ◽  
Andhika Giri Persada
Keyword(s):  
Mobile Learning ◽  
Latent Dirichlet Allocation ◽  
Educational Institution ◽  
Learning System ◽  
Educational Context ◽  
Topic Modelling ◽  
Word Cloud ◽  
Learning Research ◽  
Mobile Learning System ◽  
Modelling Approach

This paper reports a map of identified topics from mobile learning research. Mobile learning is an emerging paradigm in an educational context as its adoption in an educational institution is growing rapidly. The students are already using and familiar with it.  The publications from the last ten years were examined. Two approaches were employed to identify themes, i.e. word cloud and Latent Dirichlet Allocation. The result shows that mobile learning research is shifting from the development into optimization paradigm. This research is beneficial for mobile learning literature to inform the researcher and practitioner in the mobile learning area in terms of research topic trend and therefore consider it as a basis for designing mobile learning system in the future.

Semantic Characterization of Tweets Using Topic Models

2013 ◽  
Vol 9 (3) ◽  
pp. 1-13 ◽  
Author(s):  
Andrés García-Silva ◽  
Víctor Rodríguez-Doncel ◽  
Oscar Corch
Keyword(s):  
Natural Language ◽  
Sentiment Analysis ◽  
Latent Dirichlet Allocation ◽  
Topic Models ◽  
Probabilistic Topic Models ◽  
The Times ◽  
Past Experiences ◽  
Shed Light ◽  
Dirichlet Allocation

In the entertainment domain users tweet about their expectations and opinions regarding upcoming, current and past experiences, while companies advertise and promote the shows. This characterization, important for customers and companies, goes beyond traditional sentiment analysis where the polarity of the sentiments expressed in opinions is usually identified as positive, negative or neutral. The authors investigate different tweet representation models, including bags of words and probabilistic topic models, to shed light on the semantics of the messages. Their experiments show that topic-based models generated with Latent Dirichlet Allocation (LDA) yield, most of the times, better categorizations when compared to TF-IDF based features, particularly when these models are enriched with natural language features and specific Twitter slang.

scholarly journals Responsibility of the Netherlands for the Acts of Dutchbat in Nuhanović and Mustafić: The Continuous Quest for a Tangible Meaning for ‘Effective Control’ in the Context of Peacekeeping

2012 ◽  
Vol 25 (2) ◽  
pp. 521-535 ◽  
Author(s):  
BÉRÉNICE BOUTIN
Keyword(s):  
The Netherlands ◽  
International Law ◽  
International Organizations ◽  
Effective Control ◽  
The Hague ◽  
Court Of Appeal ◽  
Shed Light

AbstractIn Nuhanović and Mustafić (5 July 2011), the Court of Appeal of The Hague held the Netherlands liable under Bosnian torts law in relation to acts of Dutchbat in the days following the fall of Srebrenica. The claims were brought by relatives of victims killed by Mladić's troops after being evicted from the Dutchbat premises, where they had sought refuge. When resorting to international law to attribute the conduct to the Netherlands, the Court shed light on the concrete meaning of ‘effective control’ when a wrongful conduct does not result from direct orders, thereby clarifying some of the questions surrounding the determination of responsibility for conducts in the framework of international organizations.

scholarly journals Strengthening government’s response to COVID-19 in Indonesia: a modified Delphi study of medical and health academics

2020 ◽  
Author(s):  
Yodi Mahendradhata ◽  
Trisasi Lestari ◽  
Riyanti Djalante
Keyword(s):  
Public Health ◽  
Health System ◽  
Delphi Study ◽  
Contact Tracing ◽  
Government Officials ◽  
Public Health Emergencies ◽  
Government Response ◽  
Modified Delphi ◽  
Score Card ◽  
Strengthening Health System

AbstractThe Indonesian government has issued various policies to control COVID-19. However, COVID-19 new cases continued to increase and there remains uncertainties as to the future trajectory. We aimed to investigate how do medical and health academics view the Indonesian government’s handling of the COVID-19 and which area of health systems that need to be prioritized to improve government’s response to COVID-19. We conducted a modified Delphi study adapting the COVID-19 assessment score card (COVID-SCORE) as the measurement criteria. We invited medical and health academics from ten universities across Indonesia to take part in the Delphi study. In the first round, participants were presented with 20 statements of COVID-SCORE and asked to rate their agreement with each statement using five-point Likert scale. All participants who have completed the first cycle were invited to participate in the second cycle in which they had the opportunity to revise their answer based on results of previous cycle and to rank a priority of actions to improve government response. We achieved consensus for 5 statements, majority agreements for 13 statements and no consensus for 2 statements. The prioritization suggested that top priorities for improving government’s response to COVID-19 in Indonesia, according to medical and health academics, encompass: (1) The authorities communicate clearly and consistently about COVID-19 and provide public health grounds for their decisions; (2) Everyone can get a free, reliable COVID-19 test quickly and receive the results promptly; (3) Contact tracing is implemented for positive cases; (4) Public health experts, government officials, and academic researchers agree on COVID-19 nomenclature and clearly explain the reasons for public health measures; and (5) Government communications target the entire diverse population. Ultimately, our study highlights the importance of strengthening health system functions during the pandemic and to improve health system resilience for dealing with future public health emergencies.

scholarly journals Internet Rumors During the COVID-19 Pandemic: Dynamics of Topics and Public Psychologies

2021 ◽  
Vol 9 ◽  
Author(s):  
Quan Xiao ◽  
Weiling Huang ◽  
Xing Zhang ◽  
Shanshan Wan ◽  
Xia Li
Keyword(s):  
Public Health ◽  
Latent Dirichlet Allocation ◽  
The Internet ◽  
Dynamic Features ◽  
Psychosocial Characteristics ◽  
Public Health Emergencies ◽  
Depth Analysis ◽  
Human Immunity ◽  
Social Opinions ◽  
Virus Spreading

The capturing of social opinions, especially rumors, is a crucial issue in digital public health. With the outbreak of the COVID-19 pandemic, the discussions of related topics have increased exponentially in social media, with a large number of rumors on the Internet, which highly impede the harmony and sustainable development of society. As human health has never suffered a threat of this magnitude since the Internet era, past studies have lacked in-depth analysis of rumors regarding such a globally sweeping pandemic. This text-based analysis explores the dynamic features of Internet rumors during the COVID-19 pandemic considering the progress of the pandemic as time-series. Specifically, a Latent Dirichlet Allocation (LDA) model is used to extract rumor topics that spread widely during the pandemic, and the extracted six rumor topics, i.e., “Human Immunity,” “Technology R&D,” “Virus Protection,” “People's Livelihood,” “Virus Spreading,” and “Psychosomatic Health” are found to show a certain degree of concentrated distribution at different stages of the pandemic. Linguistic Inquiry and Word Count (LIWC) is used to statistically test the psychosocial dynamics reflected in the rumor texts, and the results show differences in psychosocial characteristics of rumors at different stages of the pandemic progression. There are also differences in the indicators of psychosocial characteristics between truth and disinformation. Our results reveal which topics of rumors and which psychosocial characteristics are more likely to spread at each stage of progress of the pandemic. The findings contribute to a comprehensive understanding of the changing public opinions and psychological dynamics during the pandemic, and also provide reference for public opinion responses to major public health emergencies that may arise in the future.

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