RESUMEN
During public policy information diffusion, policy interpretation on government microblogs and public attention interact, but there are certain differences. We construct a research framework for the heterogeneous diffusion of public policy information on government microblogs. An empirical study is conducted based on the Network Agenda Setting (NAS) model. First, a combination of topic mining and content analysis is used to identify the issues discussed by government microblogs and citizens. Then, we use the importance of nodes in Degree Structure (DS) and Flow Structure (FS) entropy to measure their attention to different issues. Finally, the Quadratic Assignment Procedure (QAP) correlation and regression analysis explore the degree of heterogeneity and causal relationship between government microblog agenda networks (GMANs) and public agenda networks (PANs). We find that GMANs influence PANs and the degree of heterogeneity between them is relatively low at the beginning of policy implementation. However, as government microblogs reveal positive effects of policy implementation, they fail to influence PANs effectively, and there is a greater degree of heterogeneity between them. Moreover, PANs do not significantly affect GMANs. The dynamic leading relationship between GMANs and PANs in public policy diffusion is clarified, helping to shape the image of digital government in public opinion.
RESUMEN
The outbreak of information epidemic in crisis events, with the channel effect of social media, has brought severe challenges to global public health. Combining information, users and environment, understanding how emotional information spreads on social media plays a vital role in public opinion governance and affective comfort, preventing mass incidents and stabilizing the network order. Therefore, from the perspective of the information ecology and elaboration likelihood model (ELM), this study conducted a comparative analysis based on two large-scale datasets related to COVID-19 to explore the influence mechanism of sentiment on the forwarding volume, spreading depth and network influence of information dissemination. Based on machine learning and social network methods, topics, sentiments, and network variables are extracted from large-scale text data, and the dissemination characteristics and evolution rules of online public opinions in crisis events are further analyzed. The results show that negative sentiment positively affects the volume, depth, and influence compared with positive sentiment. In addition, information characteristics such as richness, authority, and topic influence moderate the relationship between sentiment and information dissemination. Therefore, the research can build a more comprehensive connection between the emotional reaction of network users and information dissemination and analyze the internal characteristics and evolution trend of online public opinion. Then it can help sentiment management and information release strategy when emergencies occur.
RESUMEN
OBJECTIVE: To further standardize and guide the infection prevention and control(IPC) in designated hospitals so as to effectively ensure the stability, order and safety of medical treatment, ensure the safety of health care workers and patients, and reduce cross infections caused by the transmission of COVID-19. METHODS: The experts who repeatedly participated in the national COVID-19 medical treatment and IPC were invited to compile the consensus based on latest national norms, characteristics of the omicron and situation of epidemic prevention and control. RESULTS: The consensus consisted of two major parts: comprehensive coverage and control of infections in designated hospitals, with 47 recommendations involved. CONCLUSION: The expert consensus will provide guidance for the upcoming prevention and control of infection in designated hospitals.
RESUMEN
The outbreak of information epidemic in crisis events, with the channel effect of social media, has brought severe challenges to global public health. Combining information, users and environment, understanding how emotional information spreads on social media plays a vital role in public opinion governance and affective comfort, preventing mass incidents and stabilizing the network order. Therefore, from the perspective of the information ecology and elaboration likelihood model (ELM), this study conducted a comparative analysis based on two large-scale datasets related to COVID-19 to explore the influence mechanism of sentiment on the forwarding volume, spreading depth and network influence of information dissemination. Based on machine learning and social network methods, topics, sentiments, and network variables are extracted from large-scale text data, and the dissemination characteristics and evolution rules of online public opinions in crisis events are further analyzed. The results show that negative sentiment positively affects the volume, depth, and influence compared with positive sentiment. In addition, information characteristics such as richness, authority, and topic influence moderate the relationship between sentiment and information dissemination. Therefore, the research can build a more comprehensive connection between the emotional reaction of network users and information dissemination and analyze the internal characteristics and evolution trend of online public opinion. Then it can help sentiment management and information release strategy when emergencies occur.
RESUMEN
When public health emergencies occur, a large amount of low-credibility information is widely disseminated by social bots, and public sentiment is easily manipulated by social bots, which may pose a potential threat to the public opinion ecology of social media. Therefore, exploring how social bots affect the mechanism of information diffusion in social networks is a key strategy for network governance. This study combines machine learning methods and causal regression methods to explore how social bots influence information diffusion in social networks with theoretical support. Specifically, combining stakeholder perspective and emotional contagion theory, we proposed several questions and hypotheses to investigate the influence of social bots. Then, the study obtained 144,314 pieces of public opinion data related to COVID-19 in J city from March 1, 2022, to April 18, 2022, on Weibo, and selected 185,782 pieces of data related to the outbreak of COVID-19 in X city from December 9, 2021, to January 10, 2022, as supplement and verification. A comparative analysis of different data sets revealed the following findings. Firstly, through the STM topic model, it is found that some topics posted by social bots are significantly different from those posted by humans, and social bots play an important role in certain topics. Secondly, based on regression analysis, the study found that social bots tend to transmit information with negative sentiments more than positive sentiments. Thirdly, the study verifies the specific distribution of social bots in sentimental transmission through network analysis and finds that social bots are weaker than human users in the ability to spread negative sentiments. Finally, the Granger causality test is used to confirm that the sentiments of humans and bots can predict each other in time series. The results provide practical suggestions for emergency management under sudden public opinion and provide a useful reference for the identification and analysis of social bots, which is conducive to the maintenance of network security and the stability of social order.
RESUMEN
Makeshift hospitals have played an important role in responding to the spread of the epidemic caused by the Omicron coronavirus variant, one of the novel coronavirus(SARS-CoV-2) strains with significantly enhanced infectiousness. In order to prevent the patients, healthcare workers and other staff against from infection, Healthcare-associated Infection Management Committee of Chinese Hospital Association organized domestic experts to jointly formulate this consensus according to the comprehensive consideration of national guidelines as well as the actual characteristics and needs of makeshift hospitals. This consensus is mainly applicable for makeshift hospitals where a large number of asymptomatic and mild cases of novel coronavirus disease 2019(COVID-19) are treated. It provides guidance for the managers and staff to implement prevention and control work in line with local conditions in makeshift hospitals based on a perfect organizational structure and efficient working mechanism, the prevention and control work includes training and assessment of infection control knowledge and skills, flowing in and out of the makeshift hospitals for staff and materials, infection monitoring and feedback, implementation of infection prevention and control measures, requirements for infection management in key areas, occupational protection of staff and terminal disinfection, etc. Meanwhile, this consensus particularly emphasizes that the infection prevention and control in makeshift hospitals is a systematic project, which requires not only multi-system and multi-department collaboration, but also uniting in a concrete effort among leaders and staff. In accordance with the national guidelines and evidence-based experiences, it is very important to combine theory with practice for ensuring efficient operation and safety of makeshift hospitals.
RESUMEN
Synergies and trade-offs among the United Nations Sustainable Development Goals (SDGs) have been hotly debated. Although the world is increasingly metacoupled (socioeconomic-environmental interactions within and across adjacent or distant systems), there is little understanding of the impacts of globally widespread and important flows on enhancing or compromising sustainability in different systems. Here, we used a new integrated framework to guide SDG synergy and trade-off analysis within and across systems, as influenced by cross-boundary tourism and wildlife translocations. The world's terrestrial protected areas alone receive approximately 8 billion visits per year, generating a direct economic impact of US $600 billion. Globally, more than 5000 animal species and 29,000 plant species are traded across country borders, and the wildlife trade has arguably contributed to zoonotic disease worldwide, such as the ongoing COVID-19 pandemic. We synthesized 22 cases of tourism and wildlife translocations across six continents and found 33 synergies and 14 trade-offs among 10 SDGs within focal systems and across spillover systems. Our study provides an empirical demonstration of SDG interactions across spillover systems and insights for holistic sustainability governance, contributing to fostering synergies and reducing trade-offs to achieve global sustainable development in the metacoupled Anthropocene.
RESUMEN
Synergies and trade-offs among the United Nations Sustainable Development Goals (SDGs) have been hotly debated. Although the world is increasingly metacoupled (socioeconomic-environmental interactions within and across adjacent or distant systems), there is little understanding of the impacts of globally widespread and important flows on enhancing or compromising sustainability in different systems. Here, we used a new integrated framework to guide SDG synergy and trade-off analysis within and across systems, as influenced by cross-boundary tourism and wildlife translocations. The world"s terrestrial protected areas alone receive approximately 8 billion visits per year, generating a direct economic impact of US $600 billion. Globally, more than 5000 animal species and 29,000 plant species are traded across country borders, and the wildlife trade has arguably contributed to zoonotic disease worldwide, such as the ongoing COVID-19 pandemic. We synthesized 22 cases of tourism and wildlife translocations across six continents and found 33 synergies and 14 trade-offs among 10 SDGs within focal systems and across spillover systems. Our study provides an empirical demonstration of SDG interactions across spillover systems and insights for holistic sustainability governance, contributing to fostering synergies and reducing trade-offs to achieve global sustainable development in the metacoupled Anthropocene.