Epidemiological Characteristics of Novel Coronavirus COVID-19 Based on Web Data Mining

Based on the Selenium data mining technology, the epidemiological characteristics of real help cases in Sina Weibo were obtained by the analysis of 690 valid cases posted in the Sina Weibo “Pneumonia Patients Asking for Help” topic from February 4 to February 22, 2020. The research showed that 97.6% of the patients seeking for help came from Wuhan, mainly centralized in Wuchang, Tongkou, Hanyang etc. urban areas, and the proportion is directly proportional to the local medical resources and population density. The cases of Weibo help were mainly distributed from February 4 to February 7, 2020. With the relief of medical resources, the number of cases seeking help through social media decreased significantly. The distribution of patients, whose diagnosed date was mainly from January 16 to February 6, 2020, was basically consistent with the case information released by the Chinese Center for Disease Control and Prevention (CCDC). The median age of patients seeking for help was 60 years old, which was much higher than the data released by the CCDC but was roughly coincident with the data of the central hospital of Wuhan. The results of this study indicate that when dealing with major outbreaks of infectious diseases, social media are equally important in epidemiological analysis as well as the role in the dissemination of public opinion. Based on the wide adoption and timeliness nature of social media, it will be helpful for decision-makers to quickly grasp the real-world situation as it is combined with data mining or big data analysis.

E-cigarette use among youth in China

In recent years, frequent food safety incidents have resulted in significant losses to both lives and properties, triggering widespread societal concern. To delve into the mechanisms underlying the dissemination of online public opinion during food safety incidents, this research investigated a “Pork with Salted Vegetable” food safety incident in China. The study employed a comprehensive approach, integrating automated text—mining techniques with grounded theory. First, topic modeling was used to identify six dominant concerns (topics) expressed by the public during the relevant time frame, and the key words associated with each topic. Subsequently, grounded theory method was employed to model the dissemination of public opinion toward food safety incidents. The analysis emphasizes the significant roles of four key categories—“public opinion content,” “food safety incident,” “dissemination subject,” and “dissemination channel”—in the process of food safety incident dissemination. The findings offer valuable insights for food crisis governance.

In the era we live in today, the network is often used to analyze a large number of complex systems. With the development of the information society, there are more and more ways to disseminate public information through social networks. Public opinion dissemination refers to the process of disseminating public opinion information through social networks. Because the dissemination of public opinion is the basis for the exchange of ideas among multiple communicators of public opinion, the network community will certainly have an impact on the dissemination and development of public opinion. This article is based on artificial intelligence to study the network public opinion big data dissemination characteristic analysis system, introduces the network public opinion analysis system based on the characteristics of the network public opinion, introduces in detail multiple methods and clustering algorithms for extracting the text information of Internet public opinion, and proposes the Kmeans + Canopy + semantic similarity algorithm, and uses the A event to compare the parameters of the network clustering coefficient, the correlation measure and the degree centrality measure, and the performance of the Kmeans + Canopy algorithm and the Kmeans + Canopy + semantic similarity algorithm. The results of the experiment found that the clustering coefficient of “People’s Daily” in the network dissemination of A event was 0.038, which was the highest among all nodes. It shows that 3.8% of the nodes established by the “People’s Daily” can interact one-to-one to deliver information and intelligence resources. Although the complexity of the algorithm has increased and the time consumed by the system has increased, the accuracy of clustering has been improved, especially for cultural articles, the accuracy rate has been as high as 75%, and entertainment articles can reach up to 70%, and stabilize at around 70%.

Due to the potential applications in social and political science, research on public opinion formation and diffusion have been increasing for a long time. Many researchers have developed numerous dynamics models, and they have used a wide variety of statistical, computational, and mathematical methods to understand the spread of public opinion in online social networks. So far, in some standard public opinion diffusing models, the transition probability from ignorants to spreaders is always treated as a constant. However, from a realistic perspective view, an individual whether or not be infected by the neighbor spreader greatly depends on the trustiness between them. In this paper, we introduced a public opinion diffusion model with variable transition probability parameter, in which the infectious probability was partly defined as an exponent function of the spreaders, and covered probability was also defined as an exponent function of the stiflers. Furthermore, we proposed a public opinion diffusion dynamics model with isolation class for hindering the diffusion of public opinion. Finally, we investigated numerically the behavior of the proposed models. Our findings may offer some useful insights in understanding the underlying dynamics of public opinion in online social networks.

BackgroundThe COVID-19 pandemic has created a global health crisis that is affecting economies and societies worldwide. During times of uncertainty and unexpected change, people have turned to social media platforms as communication tools and primary information sources. Platforms such as Twitter and Sina Weibo have allowed communities to share discussion and emotional support; they also play important roles for individuals, governments, and organizations in exchanging information and expressing opinions. However, research that studies the main concerns expressed by social media users during the pandemic is limited.ObjectiveThe aim of this study was to examine the main concerns raised and discussed by citizens on Sina Weibo, the largest social media platform in China, during the COVID-19 pandemic.MethodsWe used a web crawler tool and a set of predefined search terms (New Coronavirus Pneumonia, New Coronavirus, and COVID-19) to investigate concerns raised by Sina Weibo users. Textual information and metadata (number of likes, comments, retweets, publishing time, and publishing location) of microblog posts published between December 1, 2019, and July 32, 2020, were collected. After segmenting the words of the collected text, we used a topic modeling technique, latent Dirichlet allocation (LDA), to identify the most common topics posted by users. We analyzed the emotional tendencies of the topics, calculated the proportional distribution of the topics, performed user behavior analysis on the topics using data collected from the number of likes, comments, and retweets, and studied the changes in user concerns and differences in participation between citizens living in different regions of mainland China.ResultsBased on the 203,191 eligible microblog posts collected, we identified 17 topics and grouped them into 8 themes. These topics were pandemic statistics, domestic epidemic, epidemics in other countries worldwide, COVID-19 treatments, medical resources, economic shock, quarantine and investigation, patients’ outcry for help, work and production resumption, psychological influence, joint prevention and control, material donation, epidemics in neighboring countries, vaccine development, fueling and saluting antiepidemic action, detection, and study resumption. The mean sentiment was positive for 11 topics and negative for 6 topics. The topic with the highest mean of retweets was domestic epidemic, while the topic with the highest mean of likes was quarantine and investigation.ConclusionsConcerns expressed by social media users are highly correlated with the evolution of the global pandemic. During the COVID-19 pandemic, social media has provided a platform for Chinese government departments and organizations to better understand public concerns and demands. Similarly, social media has provided channels to disseminate information about epidemic prevention and has influenced public attitudes and behaviors. Government departments, especially those related to health, can create appropriate policies in a timely manner through monitoring social media platforms to guide public opinion and behavior during epidemics.

PurposeSocial media, especially microblog, has become one of the most popular platforms for public opinion dissemination. However, so far few studies have been conducted to explore information dissemination under the mobile environment. This paper aims to introduce the approach to analyze the public opinion information dissemination in mobile social networks.Design/methodology/approachThis paper chooses “network attack” as the research topic and extracts 23,567 relevant messages from Sina Microblogs to study the structure of nodes for public opinion dissemination and the characteristics of propagation paths on mobile internet. Public opinion dissemination is compared on both mobile and non-mobile terminals.FindingsThe results reveal the characteristics of public opinion dissemination in mobile environment and identify three patterns of information propagation path. This study concludes that public opinion on mobile internet propagates more widely and efficiently and generates more impact than that on the non-mobile internet.Social implicationsThe methods used in this study can be useful for the government and other organizations to analyze and identify problems in online information dissemination.Originality/valueThis paper explores the mechanism of public opinion dissemination on mobile internet in China and further investigates how to improve public opinion management through a case study related to “network attack.”

In recent years, social media has become a pivotal channel for public opinion dissemination, characterized by its multi-topic nature and dynamic complexity. This study utilizes data on public health incident dissemination from the Weibo platform as its research sample and, based on a multi-layered network model, proposes a framework for topic circle identification and dynamic network analysis in public opinion dissemination. The study explores the classification of user roles within topic circles, constructs a dynamic role transition matrix, and quantitatively analyses the impact of topic circle characteristics on dissemination volume. The results indicate that core users exhibit significant stability and a dominant role within dissemination networks, while intermediate users display pronounced role mobility, and peripheral users are most sensitive to topic relevance. Furthermore, sentiment, forwarding enthusiasm, and forwarding depth exhibit significant differences in their effects on dissemination volume across different user roles. This study enriches public opinion dissemination theory by examining the dynamic evolution of topics and user role transitions, providing practical guidance for managing and controlling public opinion dissemination on social media.

Focused on the mathematical foundations of social media analysis, Graph-Based Social Media Analysis provides a comprehensive introduction to the use of graph analysis in the study of social and digital media. It addresses an important scientific and technological challenge, namely the confluence of graph analysis and network theory with linear algebra, digital media, machine learning, big data analysis, and signal processing. Supplying an overview of graph-based social media analysis, the book provides readers with a clear understanding of social media structure. It uses graph theory, particularly the algebraic description and analysis of graphs, in social media studies. The book emphasizes the big data aspects of social and digital media. It presents various approaches to storing vast amounts of data online and retrieving that data in real-time. It demystifies complex social media phenomena, such as information diffusion, marketing and recommendation systems in social media, and evolving systems. It also covers emerging trends, such as big data analysis and social media evolution. Describing how to conduct proper analysis of the social and digital media markets, the book provides insights into processing, storing, and visualizing big social media data and social graphs. It includes coverage of graphs in social and digital media, graph and hyper-graph fundamentals, mathematical foundations coming from linear algebra, algebraic graph analysis, graph clustering, community detection, graph matching, web search based on ranking, label propagation and diffusion in social media, graph-based pattern recognition and machine learning, graph-based pattern classification and dimensionality reduction, and much more. This book is an ideal reference for scientists and engineers working in social media and digital media production and distribution. It is also suitable for use as a textbook in undergraduate or graduate courses on digital media, social media, or social networks.

Lists and Social MediaLists have long been an ordering mechanism for computer-mediated social interaction. While far from being the first such mechanism, blogrolls offered an opportunity for bloggers to provide a list of their peers; the present generation of social media environments similarly provide lists of friends and followers. Where blogrolls and other earlier lists may have been user-generated, the social media lists of today are more likely to have been produced by the platforms themselves, and are of intrinsic value to the platform providers at least as much as to the users themselves; both Facebook and Twitter have highlighted the importance of their respective “social graphs” (their databases of user connections) as fundamental elements of their fledgling business models. This represents what Mejias describes as “nodocentrism,” which “renders all human interaction in terms of network dynamics (not just any network, but a digital network with a profit-driven infrastructure).”The communicative content of social media spaces is also frequently rendered in the form of lists. Famously, blogs are defined in the first place by their reverse-chronological listing of posts (Walker Rettberg), but the same is true for current social media platforms: Twitter, Facebook, and other social media platforms are inherently centred around an infinite, constantly updated and extended list of posts made by individual users and their connections.The concept of the list implies a certain degree of order, and the orderliness of content lists as provided through the latest generation of centralised social media platforms has also led to the development of more comprehensive and powerful, commercial as well as scholarly, research approaches to the study of social media. Using the example of Twitter, this article discusses the challenges of such “big data” research as it draws on the content lists provided by proprietary social media platforms.Twitter Archives for ResearchTwitter is a particularly useful source of social media data: using the Twitter API (the Application Programming Interface, which provides structured access to communication data in standardised formats) it is possible, with a little effort and sufficient technical resources, for researchers to gather very large archives of public tweets concerned with a particular topic, theme or event. Essentially, the API delivers very long lists of hundreds, thousands, or millions of tweets, and metadata about those tweets; such data can then be sliced, diced and visualised in a wide range of ways, in order to understand the dynamics of social media communication. Such research is frequently oriented around pre-existing research questions, but is typically conducted at unprecedented scale. The projects of media and communication researchers such as Papacharissi and de Fatima Oliveira, Wood and Baughman, or Lotan, et al.—to name just a handful of recent examples—rely fundamentally on Twitter datasets which now routinely comprise millions of tweets and associated metadata, collected according to a wide range of criteria. What is common to all such cases, however, is the need to make new methodological choices in the processing and analysis of such large datasets on mediated social interaction.Our own work is broadly concerned with understanding the role of social media in the contemporary media ecology, with a focus on the formation and dynamics of interest- and issues-based publics. We have mined and analysed large archives of Twitter data to understand contemporary crisis communication (Bruns et al), the role of social media in elections (Burgess and Bruns), and the nature of contemporary audience engagement with television entertainment and news media (Harrington, Highfield, and Bruns). Using a custom installation of the open source Twitter archiving tool yourTwapperkeeper, we capture and archive all the available tweets (and their associated metadata) containing a specified keyword (like “Olympics” or “dubstep”), name (Gillard, Bieber, Obama) or hashtag (#ausvotes, #royalwedding, #qldfloods). In their simplest form, such Twitter archives are commonly stored as delimited (e.g. comma- or tab-separated) text files, with each of the following values in a separate column: text: contents of the tweet itself, in 140 characters or less to_user_id: numerical ID of the tweet recipient (for @replies) from_user: screen name of the tweet sender id: numerical ID of the tweet itself from_user_id: numerical ID of the tweet sender iso_language_code: code (e.g. en, de, fr, ...) of the sender’s default language source: client software used to tweet (e.g. Web, Tweetdeck, ...) profile_image_url: URL of the tweet sender’s profile picture geo_type: format of the sender’s geographical coordinates geo_coordinates_0: first element of the geographical coordinates geo_coordinates_1: second element of the geographical coordinates created_at: tweet timestamp in human-readable format time: tweet timestamp as a numerical Unix timestampIn order to process the data, we typically run a number of our own scripts (written in the programming language Gawk) which manipulate or filter the records in various ways, and apply a series of temporal, qualitative and categorical metrics to the data, enabling us to discern patterns of activity over time, as well as to identify topics and themes, key actors, and the relations among them; in some circumstances we may also undertake further processes of filtering and close textual analysis of the content of the tweets. Network analysis (of the relationships among actors in a discussion; or among key themes) is undertaken using the open source application Gephi. While a detailed methodological discussion is beyond the scope of this article, further details and examples of our methods and tools for data analysis and visualisation, including copies of our Gawk scripts, are available on our comprehensive project website, Mapping Online Publics.In this article, we reflect on the technical, epistemological and political challenges of such uses of large-scale Twitter archives within media and communication studies research, positioning this work in the context of the phenomenon that Lev Manovich has called “big social data.” In doing so, we recognise that our empirical work on Twitter is concerned with a complex research site that is itself shaped by a complex range of human and non-human actors, within a dynamic, indeed volatile media ecology (Fuller), and using data collection and analysis methods that are in themselves deeply embedded in this ecology. “Big Social Data”As Manovich’s term implies, the Big Data paradigm has recently arrived in media, communication and cultural studies—significantly later than it did in the hard sciences, in more traditionally computational branches of social science, and perhaps even in the first wave of digital humanities research (which largely applied computational methods to pre-existing, historical “big data” corpora)—and this shift has been provoked in large part by the dramatic quantitative growth and apparently increased cultural importance of social media—hence, “big social data.” As Manovich puts it: For the first time, we can follow [the] imaginations, opinions, ideas, and feelings of hundreds of millions of people. We can see the images and the videos they create and comment on, monitor the conversations they are engaged in, read their blog posts and tweets, navigate their maps, listen to their track lists, and follow their trajectories in physical space. (Manovich 461) This moment has arrived in media, communication and cultural studies because of the increased scale of social media participation and the textual traces that this participation leaves behind—allowing researchers, equipped with digital tools and methods, to “study social and cultural processes and dynamics in new ways” (Manovich 461). However, and crucially for our purposes in this article, many of these scholarly possibilities would remain latent if it were not for the widespread availability of Open APIs for social software (including social media) platforms. APIs are technical specifications of how one software application should access another, thereby allowing the embedding or cross-publishing of social content across Websites (so that your tweets can appear in your Facebook timeline, for example), or allowing third-party developers to build additional applications on social media platforms (like the Twitter user ranking service Klout), while also allowing platform owners to impose de facto regulation on such third-party uses via the same code. While platform providers do not necessarily have scholarship in mind, the data access affordances of APIs are also available for research purposes. As Manovich notes, until very recently almost all truly “big data” approaches to social media research had been undertaken by computer scientists (464). But as part of a broader “computational turn” in the digital humanities (Berry), and because of the increased availability to non-specialists of data access and analysis tools, media, communication and cultural studies scholars are beginning to catch up. Many of the new, large-scale research projects examining the societal uses and impacts of social media—including our own—which have been initiated by various media, communication, and cultural studies research leaders around the world have begun their work by taking stock of, and often substantially extending through new development, the range of available tools and methods for data analysis. The research infrastructure developed by such projects, therefore, now reflects their own disciplinary backgrounds at least as much as it does the fundamental principles of computer science. In turn, such new and often experimental tools and methods necessarily also provoke new epistemological and methodological challenges. The Twitter API and Twitter ArchivesThe Open

The Social Media Contradiction: Data Mining and Digital Death

EditorialControl and challenge of COVID-19: lessons from China's experienceNa Zhu and Wenjie TanNa ZhuNHC Key Laboratory of Biosafety, National Institute for Viral Disease Control and Prevention, Chinese Center For Disease Control and Prevention, Beijing, China and Wenjie TanNHC Key Laboratory of Biosafety, National Institute for Viral Disease Control and Prevention, Chinese Center For Disease Control and Prevention, Beijing, ChinaCenter for Biosafety Mega-Science, Chinese Academy of Sciences, Beijing, ChinaPublished Online:08 Nov 2021https://doi.org/10.1152/ajplung.00412.2021This is the final version - click for previous versionMoreFiguresReferencesRelatedInformationSectionsINTRODUCTIONLARGE-SCALE PCR-BASED TESTINGNONPHARMACEUTICAL INTERVENTIONSCHALLENGESGRANTSDISCLOSURESAUTHOR CONTRIBUTIONSAUTHOR NOTESPDF (200 KB)Download PDF ToolsExport citationAdd to favoritesGet permissionsTrack citations ShareShare onFacebookXLinkedInWeChat INTRODUCTION We have now been living with coronavirus disease 2019 (COVID-19) for nearly 2 years. COVID-19 is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which was first identified and reported to the World Health Organization (WHO) by the China Novel Coronavirus Investigating and Research Team (1). Globally, as of September 24, 2021, there have been 230,418,451 confirmed cases of COVID-19, including 4,724,876 deaths, reported to WHO (2). Although there remain a few points of indigenous transmission in individual cities, COVID-19 is currently under control in China, successfully supported by containment and suppression strategies based on combining comprehensive large-scale polymerase chain reaction (PCR)-based testing (LSPT) and nonpharmaceutical interventions (NPIs) for control and prevention (3, 4).LARGE-SCALE PCR-BASED TESTING After the initial outbreak of COVID-19 in Wuhan city, Chinese public health, clinical, and scientific communities responded rapidly. Zhu and colleagues (1) identified the pathogen of this outbreak as a novel coronavirus that falls within the subgenus β-coronavirus and the first SARS-CoV-2 stock was isolated from human airway epithelial cells. Subsequently, specific viral nucleic acid assays using RT-PCR were quickly developed for the diagnosis of SARS-CoV-2 infection (5). These assays have been widely used for detection in both laboratory and community settings and written into the national technical guidelines of China (6). "Early detection, Early reporting, Early isolation/quarantine, and Early treatment" is the mission of "4 Earlies" given by Chinese officials and were implemented quickly and thoroughly in mainland China since the COVID-19 outbreak (4, 7). The most crucial is early detection for active case finding with case management to control and sustain containment of COVID-19. Research was conducted on SARS-CoV-2 excretion and transmission to propose optimized sample collection norms for nucleic acid testing, and implementing the strategy of the "4 Earlies" through nucleic acid screening. Comprehensive, routine, and active LSPT were widely used and played a critically important role in mainland China. Once an indicator case was found or a local outbreak was identified, LSPT combined with pooled and individual sample testing was carried out population-wide (at the whole community or city level) as well as testing samples of imported goods (3). With cost-saving, readily available, and rapid PCR testing, case-finding capacity was developed in the communities of mainland China to support full reopening of local socioeconomic activities (3, 8).NONPHARMACEUTICAL INTERVENTIONS The Chinese Center for Disease Control and Prevention (China CDC) for COVID-19 Emergency Response Strategy Team described the NPIs strategies that included containment and suppression (8). A combination of self-isolation, quarantine of close contacts, and social distancing is necessary to prevent the local transmission of SARS-CoV-2 (9). Strict movement restrictions in the outbreak area and other measures (including case isolation and quarantine) began to be introduced from January 2020 in China. The use of face masks was protective for both healthcare workers and people in the community exposed to infection by SARS-CoV-2 as well as seasonal influenza (10). Guidelines from the China CDC recommend the wearing of face masks to prevent the spread of COVID-19 in crowded public areas or transportation. With everyone wearing face masks, China has used this simple and low-cost method to successfully cutoff the path of transmission and to block off the invisible infection sources of COVID-19. Mandatory and centralized quarantine was implemented for persons detected as SARS-CoV-2 positive and their close contacts. This regulation was supported by LSPT, risk assessment and early warning, cluster epidemic analysis, and analysis of the epidemiological characteristics of asymptomatic SARS-CoV-2 infections. The 5G network has made a major contribution to nationwide epidemiological risk assessment by tracking health codes, a mobile app conducted in mainland China for more information about personal exposure to risk, and mandatory health screening in public places to curb the spread of the SARS-CoV-2 (11). Big data technology for COVID-19 has played an important role in personal tracking, surveillance, and early warning (12). So the close contacts were precisely quarantined for a reasonable period. The effectiveness of outbreak containment strategies in China based on NPIs is remarkable (13).CHALLENGES LSPT and NPI were two overarching strategies used in China to prevent the spread of SARS-CoV-2 infection. The current successful control of the epidemic of COVID-19 in mainland China has benefited from the implementation of these strategies (3, 4, 8). Although case identification and management, coupled with identification and quarantine of close contacts work well, the socioeconomic costs were very high and unsustainable in the long term (14). Emerging variants result in increased transmissibility, morbidity and mortality, and breakthrough infections (15). Now China has entered the long-term prevention stage, which maintains no or minimal indigenous transmission of SARS-CoV-2 until the population is protected through immunization with safe and effective COVID-19 vaccines. There have been recent outbreaks of indigenous transmission in Nanjing City and Yangzhou City in Jiangsu province caused by breakthrough infection of the SARS-CoV-2 Delta variant (16). Hence, continuation of long-term containment measures is necessary. Vaccine effectiveness and vaccine hesitancy will be great challenges in the future. Vaccination efforts have to contend with rapidly spreading SARS-CoV-2 variants. Furthermore, as a member of the global village, China should work hand in hand with other countries, share resources and experience, strengthen cooperation, and strive to achieve the final victory in the fight against the COVID-19.GRANTS This work was suppored by the National Natural Science Foundation of China (Grant 82072296).DISCLOSURES No conflicts of interest, financial or otherwise, are declared by the authors.AUTHOR CONTRIBUTIONS N.Z. drafted manuscript; N.Z. and W.T. edited and revised manuscript; N.Z. and W.T. approved final version of manuscript.AUTHOR NOTESCorrespondence: W. Tan (tanwj@ivdc.chinacdc.cn). Download PDF Previous Back to Top Next FiguresReferencesRelatedInformationREFERENCES1. Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, Zhao X, Huang B, Shi W, Lu R, Niu P, Zhan F, Ma X, Wang D, Xu W, Wu G, Gao GF, Tan W; China Novel Coronavirus Investigating and Research Team. A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med 382: 727–733, 2020. doi:10.1056/NEJMoa2001017. Crossref | PubMed | Web of Science | Google Scholar2. World Health Organization. Weekly epidemiological update on COVID-19 (Online). https://www.who.int/publications/m/item/weekly-epidemiological-update-on-covid-19-28-september-2021 [2021 Sep 28].Google Scholar3. Li Z, Liu F, Cui J, Peng Z, Chang Z, Lai S, Chen Q, Wang L, Gao GF, Feng Z. Comprehensive large-scale nucleic acid-testing strategies support China's sustained containment of COVID-19. Nat Med 27: 740–742, 2021. doi:10.1038/s41591-021-01308-7. Crossref | PubMed | Web of Science | Google Scholar4. 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ArXiv, 2021. arXiv:2109.04509/.Google Scholar CollectionsAPS Cross-Journal CollectionsCoronavirus-Related Papers Related ArticlesWorld health day observances in November 2021: advocating for adult and pediatric pneumonia, preterm birth, and chronic obstructive pulmonary disease 08 Nov 2021American Journal of Physiology-Lung Cellular and Molecular PhysiologyCited ByMultiple impacts of the COVID-19 pandemic and antimicrobial stewardship on antimicrobial resistance in nosocomial infections: an interrupted time series analysis17 July 2024 | Frontiers in Public Health, Vol. 12Evaluating the Demand for Nucleic Acid Testing in Different Scenarios of COVID-19 Transmission: A Simulation Study18 March 2024 | Infectious Diseases and Therapy, Vol. 13, No. 4Neoadjuvant Chemotherapy Is Effective in Those Infected With SARS-CoV-2: The Real-World Experience of a Large Chinese Breast Cancer Center1 Jan 2024 | Journal of Breast Cancer, Vol. 27, No. 3Change from low to out‐of‐season epidemics of influenza in China during the COVID‐19 pandemic: A time series study20 June 2023 | Journal of Medical Virology, Vol. 95, No. 6Impact of combination preventative interventions on hospitalization and death under the pandemic of SARS‐CoV‐2 Omicron variant in China13 December 2022 | Journal of Medical Virology, Vol. 95, No. 1World health day observances in November 2022: pneumonia, chronic obstructive pulmonary disease, preterm birth, and antimicrobial resistance in focusMiša Gunjak and Rory E. Morty9 November 2022 | American Journal of Physiology-Lung Cellular and Molecular Physiology, Vol. 323, No. 5World health day observances in November 2021: advocating for adult and pediatric pneumonia, preterm birth, and chronic obstructive pulmonary diseaseRory E. Morty8 November 2021 | American Journal of Physiology-Lung Cellular and Molecular Physiology, Vol. 321, No. 5 More from this issue > Volume 321Issue 5November 2021Pages L958-L959 Crossmark Copyright & PermissionsCopyright © 2021 the American Physiological Society.https://doi.org/10.1152/ajplung.00412.2021PubMed34643094History Received 6 October 2021 Accepted 7 October 2021 Published online 8 November 2021 Published in print 1 November 2021 Keywordscontrolcoronavirus disease 2019 (COVID-19)large-scale polymerase chain reaction (PCR)-based testing (LSPT)nonpharmaceutical interventions (NPIs)severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Metrics See more details Posted by 6 X users 10 readers on Mendeley 8 CITATIONS 8 Total citations 5 Recent citations 3.27 Field Citation Ratio 0.58 Relative Citation Ratio publications8supporting0mentioning6contrasting0Smart Citations8060Citing PublicationsSupportingMentioningContrastingView CitationsSee how this article has been cited at scite.aiscite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made. 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Social Media in Mainland China: Weak Democracy, Emergent Civil Society

Monitoring infectious diseases in the big data era

BackgroundSocial media has penetrated all walks of life. Chinese health care institutions are increasingly utilizing social media to connect with their patients for better health service delivery. Current research has focused heavily on the use of social media in developed countries, with few studies exploring its usage in the context of developing countries, such as China. Tertiary hospitals in China are usually located in city centers, and they serve as medical hubs for multiple regions, with comprehensive and specialized medical care being provided. These hospitals are assumed to be the pioneers in creating official social media accounts to connect with their patients due to the fact that they appear to have more resources to support this innovative approach to communication and health care education. ObjectiveThe objective of our study was to examine China’s best tertiary hospitals, as recognized by The National Health Commission of the People’s Republic of China (NHCPRC), and to map out the landscape of current social media usage by hospitals when engaging with patients.MethodsWe examined the best 705 tertiary hospitals in China by collecting and analyzing data regarding their usage of popular Chinese social media apps Sina Weibo and WeChat. The specific data included (1) hospital characteristics (ie, time since established, number of beds, hospital type, and regions or localities) and (2) status of social media usage regarding two of the most popular local social media platforms in China (ie, time of initiation, number of followers, and number of tweets or posts). We further used a logistic regression model to test the association between hospital characteristics and social media adoption.ResultsOf all, 76.2% (537/705) tertiary referral hospitals have created official accounts on either Sina Weibo or WeChat, with the latter being more popular among the two. In addition, our study suggests that larger and newer hospitals with greater resources are more likely to adopt social media, while hospital type and affiliation with universities are not significant predictors of social media adoption among hospitals.ConclusionsOur study demonstrated that hospitals are more inclined to use WeChat. The move by hospitals from Sina Weibo to WeChat indicates that patients are not satisfied by mere communication and that they now place more value on health service delivery. Meanwhile, utilizing social media requires comprehensive thinking from the hospital side. Once adopted, hospitals are encouraged to implement specific rules regarding social media usage. In the future, a long journey still lies ahead for hospitals in terms of operating their official social media accounts.