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.

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.

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.

Trending topics and themes in offsite construction(OSC) research

2019 ◽  
Vol 19 (3) ◽  
pp. 343-366 ◽  
Author(s):  
Guiwen Liu ◽  
Juma Hamisi Nzige ◽  
Kaijian Li
Keyword(s):  
Latent Dirichlet Allocation ◽  
Information Modeling ◽  
Funding Agency ◽  
Future Research ◽  
Allocation Model ◽  
Content Type ◽  
Latent Dirichlet Allocation Model ◽  
Similar Information ◽  
Building Information ◽  
The Impact

Purpose The purpose of this study is to discover the distribution and trends of existing Offsite construction (OSC) literature with an intention to highlight research niches and propose the future outline. Design/methodology/approach The paper adopted literature reviews methodology involving 1,057 relevant documents published in 2008-2017 from 15 journals. The selected documents were empirically analyzed through a topic-modeling technique. A latent Dirichlet allocation model was applied to each document to infer 50 key topics. A machine learning for language toolkit was used to get topic posterior word distribution and word composition. Findings This is an exploratory study, which identifies the distribution of topics and themes; the trend of topics and themes; journal distribution trends; and comparative topic, themes and journal distribution trend. The distribution and trends show an increase in researcher’s interest and the journal’s priority on OSC research. Nevertheless, OSC existing literature is faced with; under-researched topics such as building information modeling, smart construction and marketing. The under-researched themes include organizational management, supply chain and context. The authors also found an overload of similar information in prefabrication and concrete topics. Furthermore, the innovative methods and constraints themes were found to be overloaded with similar information. Research limitations/implications The naming of the themes was based on our own interpretation; hence, the research results may lack generalizability. Therefore, a comparative study using different data processing is proposed. The study also provides future research outline as follows: studying OSC topics from dynamic evolution perspective and identifying the new emerging topics; searching for effective strategies to enhance OSC research; identifying the contribution of countries, affiliation and funding agency; and studying the impact of these themes to the adoption of OSC. Practical implications This study is of values to the scholars, as it could stimulate research to under-researched areas. Originality/value This paper justifies a need to have a broad understanding of the nature and structure of existing OSC literature.

scholarly journals 18.H. Workshop: Bringing evidence during public health emergencies: addressing the challenge through HTA

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
◽  
Keyword(s):  
Public Health ◽  
Decision Making ◽  
Emergency Preparedness ◽  
Public Health Emergency ◽  
Decision Makers ◽  
Evidence Based ◽  
Public Health Emergencies ◽  
Response Planning ◽  
Emergency Preparedness And Response ◽  
Public Health Emergency Preparedness

Abstract Evidence-based decision-making is central to public health. Implementing evidence-informed actions is most challenging during a public health emergency as in an epidemic, when time is limited, scientific uncertainties and political pressures tend to be high, and irrefutable evidence may be lacking. The process of including evidence in public health decision-making and for evidence-informed policy, in preparation, and during public health emergencies, is not systematic and is complicated by many barriers as the absences of shared tools and approaches for evidence-based preparedness and response planning. Many of today's public health crises are also cross-border, and countries need to collaborate in a systematic and standardized way in order to enhance interoperability and to implement coordinated evidence-based response plans. To strengthen the impact of scientific evidence on decision-making for public health emergency preparedness and response, it is necessary to better define mechanisms through which interdisciplinary evidence feeds into decision-making processes during public health emergencies and the context in which these mechanisms operate. As a multidisciplinary, standardized and evidence-based decision-making tool, Health Technology Assessment (HTA) represents and approach that can inform public health emergency preparedness and response planning processes; it can also provide meaningful insights on existing preparedness structures, working as bridge between scientists and decision-makers, easing knowledge transition and translation to ensure that evidence is effectively integrated into decision-making contexts. HTA can address the link between scientific evidence and decision-making in public health emergencies, and overcome the key challenges faced by public health experts when advising decision makers, including strengthening and accelerating knowledge transfer through rapid HTA, improving networking between actors and disciplines. It may allow a 360° perspective, providing a comprehensive view to decision-making in preparation and during public health emergencies. The objective of the workshop is to explore and present how HTA can be used as a shared and systematic evidence-based tool for Public Health Emergency Preparedness and Response, in order to enable stakeholders and decision makers taking actions based on the best available evidence through a process which is systematic and transparent. Key messages There are many barriers and no shared mechanisms to bring evidence in decision-making during public health emergencies. HTA can represent the tool to bring evidence-informed actions in public health emergency preparedness and response.

scholarly journals The challenge of bringing evidence in decision-making for public health emergencies: can preparedness reach out to HTA?

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
C E Chronaki ◽  
A Miglietta
Keyword(s):  
Public Health ◽  
Decision Making ◽  
Policy Development ◽  
Digital Health ◽  
Resource Planning ◽  
Public Health Emergency ◽  
Evidence Based ◽  
Public Health Emergencies ◽  
The Impact ◽  
Evidence Based Decision Making

Abstract Evidence-based decision-making is central to public health. Implementing evidence-informed actions is most challenging during a public health emergency as in an epidemic, when time is limited, scientific uncertainties and political pressures tend to be high, and reliable data is typically lacking. The process of including data for preparedness and training for evidence-based decision making in public health emergencies is not systematic and is complicated by many barriers as the absence of common digital tools and approaches for resource planning and update of response plans. Health Technology Assessment (HTA) is used with the aim to improve the quality and efficiency of public health interventions and to make healthcare systems more sustainable. Many of today's public health crises are also cross-border, and countries need to collaborate in a systematic and standardized way in order to enhance interoperability to share data and to plan coordinated response. Digital health tools have an important role to play in this setting, facilitating use of knowledge about the population that can potentially affected by the crisis within and across regional and national borders. To strengthen the impact of scientific evidence on decision-making for public health emergency preparedness and response, it is necessary to better define and align mechanisms through which interdisciplinary evidence feeds into decision-making processes during public health emergencies and the context in which these mechanisms operate. Activities and policy development in the HTA network could inform this process. The objective of this presentation is to identify barriers for evidence-based decision making during public health emergencies and discuss how standardization in digital health and HTA processes may help overcome these barriers leading to more effective coordinated and evidence-based public health emergency response.

scholarly journals Tripartite Evolutionary Game Model for Public Health Emergencies

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zhiqi Xu ◽  
Yukun Cheng ◽  
Shuangliang Yao
Keyword(s):  
Public Health ◽  
Local Government ◽  
Evolutionary Game ◽  
Public Health Emergency ◽  
Game Model ◽  
Negative Behaviors ◽  
The Public ◽  
Public Health Emergencies ◽  
Theoretical Support ◽  
Safety And Health

Public health emergencies are more related to the safety and health of the public. For the management of the public health emergencies, all parties’ cooperation is the key to preventing and controlling the emergencies. Based on the assumption of bounded rationality, we formulate a tripartite evolutionary game model, involving the local government, the enterprises, and the public, for the public health emergency, e.g., COVID-19. The evolutionary stable strategies under different conditions of the tripartite evolutionary game are explored, and the effect from different factors on the decision-makings of participants for public health emergencies is also analyzed. Numerical analysis results show that formulating reasonable subsidy measures, encouraging the participation of the public, and enforcing the punishment to enterprises for their negative behaviors can prompt three parties to cooperate in fighting against the epidemic. Our work enriches an understanding of the governance for the public health emergency and provides theoretical support for the local government and related participants to make proper decisions in public health emergencies.

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