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.

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 Topic Relevance of Public Health Emergencies Influence on Internet Public Opinion Resonance: Simulation Based on Langevin’s Equation

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Quan Cheng ◽  
Yan-gang Zhang ◽  
Yi-quan Li
Keyword(s):  
Public Health ◽  
Public Opinion ◽  
Stochastic Resonance ◽  
Resonance Phenomenon ◽  
The Internet ◽  
Resonance Model ◽  
Mutual Correlation ◽  
The Public ◽  
Public Health Emergencies ◽  
Topic Relevance

Public health emergencies occurred frequently, which usually result in the negative Internet public opinion events. In the complex network information ecological environment, multiple public opinion events may be aggregated to generate public opinion resonance due to the topic category, the mutual correlation of the subject involved, and the compound accumulation of specific emotions. In order to reveal the phenomenon and regulations of the public opinion resonance, we firstly analyze the influence factors of the Internet public opinion events in the public health emergencies. Then, based on Langevin’s equation, we propose the Internet public opinion stochastic resonance model considering the topic relevance. Furthermore, three exact public health emergencies in China are provided to reveal the regulations of evoked events “revival” caused by original events. We observe that the Langevin stochastic resonance model considering topic relevance can effectively reveal the resonance phenomenon of Internet public opinion caused by public health emergencies. For the original model without considering the topic relevance, the new model is more sensitive. Meanwhile, it is found that the degree of topic relevance between public health emergencies has a significant positive correlation with the intensity of Internet public opinion resonance.

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 A Peer Assessment Approach for Learning from Public Health Emergencies

2014 ◽  
Vol 129 (6_suppl4) ◽  
pp. 28-34 ◽  
Author(s):  
Rachael N. Piltch-Loeb ◽  
Christopher D. Nelson ◽  
John D. Kraemer ◽  
Elena Savoia ◽  
Michael A. Stoto
Keyword(s):  
Public Health ◽  
Emergency Preparedness ◽  
Peer Assessment ◽  
Field Tests ◽  
Advisory Panel ◽  
Public Health Emergencies ◽  
Depth Analysis ◽  
Assessment Approach ◽  
Standard Quality ◽  
Public Health Emergency Preparedness

As an alternative to standard quality improvement approaches and to commonly used after action report/improvement plans, we developed and tested a peer assessment approach for learning from singular public health emergencies. In this approach, health departments engage peers to analyze critical incidents, with the goal of aiding organizational learning within and across public health emergency preparedness systems. We systematically reviewed the literature in this area, formed a practitioner advisory panel to help translate these methods into a protocol, applied it retrospectively to case studies, and later field-tested the protocol in two locations. These field tests and the views of the health professionals who participated in them suggest that this peer-assessment approach is feasible and leads to a more in-depth analysis than standard methods. Engaging people involved in operating emergency health systems capitalizes on their professional expertise and provides an opportunity to identify transferable best practices.

An in-depth analysis of 10 epidemiological terminologies used in the context of COVID-19

2021 ◽  
pp. 140349482110577
Author(s):  
Sathyanarayanan Doraiswamy ◽  
Ravinder Mamtani ◽  
Sohaila Cheema
Keyword(s):  
Public Health ◽  
Herd Immunity ◽  
Case Fatality ◽  
Lessons Learned ◽  
Public Health Approach ◽  
Fatality Rate ◽  
Public Health Emergencies ◽  
Depth Analysis ◽  
Novel Coronavirus ◽  
Epidemiology And Public Health

Aim: In this paper, we explore the contextual use of 10 epidemiological terminologies, their significance, and interpretation/misinterpretation in explaining various aspects of the 2019 novel coronavirus disease (COVID-19) pandemic. Methods and Results: We first establish the different purposes of the terms ‘pandemic’ and ‘Public Health Emergency of International Concern.’ We then discuss the confusion caused by using the ‘case fatality rate’ as opposed to ‘infection fatality rate’ during the pandemic and the uncertainty surrounding the limited usefulness of identifying someone as ‘pre-symptomatic.’ We highlight the ambiguity in the ‘positivity rate’ and the need to be able to generate data on ‘excess mortality’ during public health emergencies. We discuss the relevance of ‘association and causation’ in the context of the facemask controversy that existed at the start of the pandemic. We point out how the accepted epidemiological practice of discussing ‘herd immunity’ in the context of vaccines has been twisted to suit the political motive of a public health approach. Given that a high proportion of COVID-19 cases are asymptomatic, we go on to show how COVID-19 has blurred the lines between ‘screening/diagnosis’ and ‘quarantine/isolation,’ while giving birth to the new terminology of ‘community quarantine.’ Conclusions: Applying the lessons learned from COVID-19 to better understand the above terminologies will help health professionals communicate effectively, strengthen the scientific agenda of epidemiology and public health, and support and manage future outbreaks efficiently.

Internet public opinion dissemination mechanism of COVID-19: evidence from the Shuanghuanglian event

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xing Zhang ◽  
Yan Zhou ◽  
Fuli Zhou ◽  
Saurabh Pratap
Keyword(s):  
Public Health ◽  
Public Opinion ◽  
Simulation Analysis ◽  
Decisive Role ◽  
The Internet ◽  
Propagation Mechanism ◽  
Evolution System ◽  
Content Type ◽  
Public Health Emergencies ◽  
The Government

PurposeThe sudden outbreak of COVID-19 has become a major public health emergency of global concern. Studying the Internet public opinion dissemination mechanism of public health emergencies is of great significance for creating a legalized network environment, and it is also helpful for managers to make scientific decisions when encountering Internet public opinion crisis.Design/methodology/approachBased on the analysis of the process of spreading the Internet public opinion in major epidemics, a dynamic model of the Internet public opinion spread system was constructed to study the interactive relationship among the public opinion events, network media, netizens and government and the spread of epidemic public opinion. The Shuanghuanglian event in COVID-19 in China was taken as a typical example to make simulation analysis.FindingsResearch results show three points: (1) the government credibility plays a decisive role in the spread of Internet public opinion; (2) it is the best time to intervene when Internet public opinion occurred at first time; (3) the management and control of social media are the key to public opinion governance. Besides, specific countermeasures are proposed to assist control of Internet public opinion dissemination.Originality/valueThe epidemic Internet public opinion risk evolution system is a complex nonlinear social system. The system dynamics model is used to carry out research to facilitate the analysis of the Internet public opinion propagation mechanism and explore the interrelationship of various factors.

scholarly journals Application of Artificial Intelligence to Social Governance Capabilities under Public Health Emergencies

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yafang Wu ◽  
Shaonan Shan
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Big Data ◽  
Emergency Management ◽  
Information Leakage ◽  
Personal Privacy ◽  
Standard Data ◽  
Public Health Emergencies ◽  
Social Governance ◽  
Depth Analysis

Due to the high complexity, high destructive power, and comprehensive governance characteristics of public health emergencies, the ability of social governance has been distorted and alienated under intensive pressure, and the subjects of social governance have become lazy, professional, and politicized. There are obvious problems, such as system information leakage and information asymmetry. Based on the above background, the purpose of this article is to study the application of artificial intelligence to social governance capabilities under public health emergencies. This article focuses on the relevant concepts and content of emergency management of public health emergencies and in-depth analysis of the actual application of big data technology in epidemic traceability and prediction, medical diagnosis and vaccine research and development, people’s livelihood services, and government advice and suggestions, combined with investigations. The questionnaire analysis sorted out the problems in the social emergency management of public health emergencies in China. The results showed that 87.7% of the people simply sorted out laws and regulations and higher-level documents or even repeated content and lacked summary and reflection on emergency response experience, which led to the operability of emergency plans being generally even poor. In response to the shortcomings, countermeasures and suggestions were put forward, including establishing a standard data collection mechanism, establishing a data sharing mechanism, establishing a personal privacy security protection mechanism, and promoting the breadth and depth of big data applications.

Topical evolution patterns and temporal trends of microblogs on public health emergencies

2018 ◽  
Vol 42 (6) ◽  
pp. 821-846
Author(s):  
Lu An ◽  
Chuanming Yu ◽  
Xia Lin ◽  
Tingyao Du ◽  
Liqin Zhou ◽  
...  
Keyword(s):  
Public Health ◽  
Information Needs ◽  
Latent Dirichlet Allocation ◽  
Temporal Trends ◽  
Public Health Emergency ◽  
Self Organizing Map ◽  
Allocation Model ◽  
Content Type ◽  
Latent Dirichlet Allocation Model ◽  
Public Health Emergencies

Purpose The purpose of this paper is to identify salient topic categories and outline their evolution patterns and temporal trends in microblogs on a public health emergency across different stages. Comparisons were also examined to reveal the similarities and differences between those patterns and trends on microblog platforms of different languages and from different nations. Design/methodology/approach A total of 459,266 microblog entries about the Ebola outbreak in West Africa in 2014 on Twitter and Weibo were collected for nine months after the inception of the outbreak. Topics were detected by the latent Dirichlet allocation model and classified into several categories. The daily tweets were analyzed with the self-organizing map technique and labeled with the most salient topics. The investigated time span was divided into three stages, and the most salient topic categories were identified for each stage. Findings In total, 14 salient topic categories were identified in microblogs about the Ebola outbreak and were summarized as increasing, decreasing, fluctuating or ephemeral types. The topical evolution patterns of microblogs and temporal trends for topic categories vary on different microblog platforms. Twitter users were keen on the dynamics of the Ebola outbreak, such as status description, secondary events and so forth, while Weibo users focused on background knowledge of Ebola and precautions. Originality/value This study revealed evolution patterns and temporal trends of microblog topics on a public health emergency. The findings can help administrators of public health emergencies and microblog communities work together to better satisfy information needs and physical demands by the public when public health emergencies are in progress.

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