Hashtag framing and stakeholder targeting: An affordance perspective on China's digital public diplomacy campaign during COVID-19

Social media platforms such as Twitter provide opportunities for governments to connect to foreign publics and influence global public opinion. In the current study, we used social and semantic network analysis to investigate China's digital public diplomacy campaign during COVID-19. Our results show that Chinese state-affiliated media and diplomatic accounts created hashtag frames and targeted stakeholders to challenge the United States or to cooperate with other countries and international organizations, especially the World Health Organization. Telling China's stories was the central theme of the digital campaign. From the perspective of social media platform affordance, we addressed the lack of attention paid to hashtag framing and stakeholder targeting in the public diplomacy literature. [ FROM AUTHOR] Copyright of Journal of Information Technology & Politics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

A social network analysis approach to estimate export disruption spread in the US during the Covid-19 pandemic: how policy response and industry ties relate

We examine how the Covid-19 pandemic led to the propagation of export disruptions on a state-by-state basis using a social network analysis model. We measure the impact of import disruptions, Covid-related hospitalizations, subsequent policy responses, and structural network effects on economic outcomes. In addition to examining contemporaneous effects, we include lagged policy response variables to determine their effect on disruption recovery trends. Findings suggest that disruptions cluster along shared industry connections. The results are consistent with previous work that shows that non-pharmaceutical policy interventions had limited contemporaneous and medium-term effects on trade flows. © 2023, The Author(s) under exclusive licence to Associazione Amici di Economia e Politica Industriale.

Network analysis of eating disorder and depression symptoms among university students in the late stage of COVID-19 pandemic in China.

Background: Eating disorders (EDs) and depression are common in university students, especially during the COVID-19 pandemic. The aim of this study was to elucidate characteristics of EDs and depression symptoms networks among Chinese university students in the later stage of the COVID-19 pandemic in China. Methods: A total of 929 university students completed the SCOFF questionnaire measuring EDs and Patient Health Questionnaire with 9 items (PHQ-9) measuring depression in Guangzhou, China. The network model was applied to identify central symptoms, bridge symptoms, and important connections between SCOFF and PHQ-9 using R studio. The subgroup analyses of both genders in medical and non-medical students were further explored. Results: In the networks of the whole sample, central symptoms included "Loss of control over eating" (EDs) and "Appetite changes" (depression). The bridge connections were between "Loss of control over eating" (EDs) and "Appetite changes" (depression), between "Deliberate vomiting" (EDs) and "Thoughts of death" (depression). "Appetite changes" (depression) and "Feeling of worthlessness" (depression) were central symptoms in both subgroups of medical and non-medical students. "Fatigue" (depression) was the central symptom in the female and medical students group. The edge between "Loss of control over eating" (EDs) and "Appetite changes" (depression) acted as a bridge in all subgroups. Conclusion: Social network approaches offered promising ways of further understanding the association between EDs and depression among university students during the pandemic of COVID-19 in China. Investigations targeting central and bridge symptoms would help to develop effective treatments for both EDs and depression for this population.

Understanding Changes in Social Cannabis Use among Young Adults During the COVID-19 Pandemic: A Social Network Analysis.

Introduction: As the COVID-19 pandemic has caused historic morbidity and mortality and disrupted young people's social relationships, little is known regarding change in young adults' social cannabis use following social distancing orders, or other factors associated with such changes before and during the pandemic. Methods: 108 young adult cannabis users in Los Angeles reported on their personal (egocentric) social network characteristics, cannabis use, and pandemic-related variables before (July 2019 - March 2020) and during the COVID-19 pandemic (August 2020 - August 2021). Multinomial logistic regression identified factors associated with increasing or maintaining the number of network members (alters) participants used cannabis with before and during the pandemic. Multilevel modeling identified ego- and alter-level factors associated with dyadic cannabis use between each ego and alter during the pandemic. Results: Most participants (61%) decreased the number of alters they used cannabis with, 14% maintained, and 25% increased. Larger networks were associated with a lower risk of increasing (vs. decreasing); more supportive cannabis-using alters was associated with a lower risk of maintaining (vs. decreasing); relationship duration was associated with a greater risk of maintaining and increasing (vs. decreasing). During the COVID-19 pandemic (August 2020 - August 2021), participants were more likely to use cannabis with alters they also used alcohol with and alters who were perceived to have more positive attitudes towards cannabis. Conclusions: The present study identifies significant factors associated with changes in young adults' social cannabis use following pandemic-related social distancing. These findings may inform social network interventions for young adults who use cannabis with their network members amid such social restrictions.

FCMCPS-COVID: AI propelled fog-cloud inspired scalable medical cyber-physical system, specific to coronavirus disease.

Medical cyber-physical systems (MCPS) firmly integrate a network of medical objects. These systems are highly efficacious and have been progressively used in the Healthcare 4.0 to achieve continuous high-quality services. Healthcare 4.0 encompasses numerous emerging technologies and their applications have been realized in the monitoring of a variety of virus outbreaks. As a growing healthcare trend, coronavirus disease (COVID-19) can be cured and its spread can be prevented using MCPS. This virus spreads from human to human and can have devastating consequences. Moreover, with the alarmingly rising death rate and new cases across the world, there is an urgent need for continuous identification and screening of infected patients to mitigate their spread. Motivated by the facts, we propose a framework for early detection, prevention, and control of the COVID-19 outbreak by using novel Industry 5.0 technologies. The proposed framework uses a dimensionality reduction technique in the fog layer, allowing high-quality data to be used for classification purposes. The fog layer also uses the ensemble learning-based data classification technique for the detection of COVID-19 patients based on the symptomatic dataset. In addition, in the cloud layer, social network analysis (SNA) has been performed to control the spread of COVID-19. The experimental results reveal that compared with state-of-the-art methods, the proposed framework achieves better results in terms of accuracy (82.28 %), specificity (91.42 %), sensitivity (90 %) and stability with effective response time. Furthermore, the utilization of CVI-based alert generation at the fog layer improves the novelty aspects of the proposed system.

WHAT WE WISH WE KNEW BEFORE GOING ONLINE: Lessons Learned From Implementing an Online MBA

Montclair State University (MSU) is New Jerseys second-largest public institution. As online education continues its rapid-paced growth, MBA programs have been some of the most common online degrees. In 2016, Montclairs Feliciano School of Business entered this crowded online MBA market. After a false start and sometimes rocky development, the online MBA was successfully launched in the fall of 2016. The program grew so fast that the leadership team needed to find innovative ways to handle the number of students. The lessons learned by the online MBA leadership team are detailed below.

Reexamining the Echo Chamber Effect of COVID-19 Vaccine Videos on YouTube

The COVID-19 pandemic has been going on for more than two years. Vaccination is believed to be one of the most efficient ways to achieve herd immunity and end pandemic. However, the contents about COVID-19 vaccines on social media have impacts on personal attitude towards vaccination. The present study aims to examine the current scenario and the echo chamber effect of COVID-19 vaccine videos on YouTube. A total of 1,646 videos with comments and replies were identified. An approach combining topic modeling, sentiment analysis, and social network analysis was employed to explore users' attitude towards COVID-19 vaccines and whether the echo chamber effect existed. The results indicate that, even if the misleading and anti-vaccination videos were removed by the platform, "anti-vaccination"contents still widely appear in the comments. Moreover, the community of "anti-vaccination"users was more homogeneous compared with that of "pro-vaccination"users. The findings of this study advanced theories of echo chamber effect and the network perspective to examine echo chambers. We propose that should be paid more attention ideology echo chamber, compared with exposure echo chamber. © 2022 IEEE.

Exploring the topic structure and evolutionary trends of health informatics research in library and information science

PurposeThis study aims to reveal the topic structure and evolutionary trends of health informatics research in library and information science.Design/methodology/approachUsing publications in Web of Science core collection, this study combines informetrics and content analysis to reveal the topic structure and evolutionary trends of health informatics research in library and information science. The analyses are conducted by Pajek, VOSviewer and Gephi.FindingsThe health informatics research in library and information science can be divided into five subcommunities: health information needs and seeking behavior, application of bibliometrics in medicine, health information literacy, health information in social media and electronic health records. Research on health information literacy and health information in social media is the core of research. Most topics had a clear and continuous evolutionary venation. In the future, health information literacy and health information in social media will tend to be the mainstream. There is room for systematic development of research on health information needs and seeking behavior.Originality/valueTo the best of the authors' knowledge, this is the first study to analyze the topic structure and evolutionary trends of health informatics research based on the perspective of library and information science. This study helps identify the concerns and contributions of library and information science to health informatics research and provides compelling evidence for researchers to understand the current state of research.

A Social Network Analysis Approach to Evaluate the Relationship Between the Mobility Network Metrics and the COVID-19 Outbreak.

The emergence of the new coronavirus in late 2019 further highlighted the human need for solutions to explore various aspects of deadly pandemics. Providing these solutions will enable humans to be more prepared for dealing with possible future pandemics. In addition, it helps governments implement strategies to tackle and control infectious diseases similar to COVID-19 faster than ever before. In this article, we used the social network analysis (SNA) method to identify high-risk areas of the new coronavirus in Iran. First, we developed the mobility network through the transfer of passengers (edges) between the provinces (nodes) of Iran and then evaluated the in-degree and page rank centralities of the network. Next, we developed 2 Poisson regression (PR) models to predict high-risk areas of the disease in different populations (moderator) using the mobility network centralities (independent variables) and the number of patients (dependent variable). The P-value of .001 for both prediction models confirmed a meaningful interaction between our variables. Besides, the PR models revealed that in higher populations, with the increase of network centralities, the number of patients increases at a higher rate than in lower populations, and vice versa. In conclusion, our method helps governments impose more restrictions on high-risk areas to handle the COVID-19 outbreak and provides a viable solution for accelerating operations against future pandemics similar to the coronavirus.

Democracy in the Indonesian Digital Public Sphere: Social Network Analysis of Twitter Users' Responses to the Issue of Nationalism Knowledge Test at the Corruption Eradication Commission (TWK-KPK)

The mobility restriction during the COVID-19 pandemic did not stop the public from expressing their opinions. Since they could not go on demonstrations, they moved democracy to the digital sphere, such as on Twitter. Previous research has shown that Twitter users in Indonesia use the platform to express political views and opinions on governmental issues. The issue of the Nationalism Knowledge Test (TWK) at the Corruption Eradication Commission (KPK) was a trending topic on Twitter for a while. The issue spurred discussions on Twitter when 75 employees did not pass the KPK-TWK on May 2021. The discussion then stopped for a moment before picking up again during the official dismissal of the employees on 30 September 2021. This article focuses on the social network analysis of the public's responses to this issue on Twitter. Social network data were collected using Drone Emprit from May to October 2021 and analyzed using Gephi to generate graphical representations of the social networks. The results reveal the structure of the movement was centralized and dynamic. Regarding the dissemination of information, the most central was news media and anti-corruption activists' accounts. These accounts mobilized the community on Twitter to make a critical social movement. This means that the digital sphere can be an evolution of democracy form and activism, especially in the anti-corruption movement. © 2022 Rev. Archai. All rights reserved.

Smart Methods to Deal with COVID-19 at University-Level Institutions Using Social Network Analysis Techniques

The current global health crisis is a consequence of the pandemic caused by COVID-19. It has impacted the lives of people from all factions of society. The re-emergence of new variants is threatening the world, which urges the development of new methods to prevent rapid spread. Places with more extensive social dealings, such as offices, organizations, and educational institutes, have a greater tendency to escalate the viral spread. This research focuses on developing a strategy to find out the key transmitters of the virus, particularly at educational institutes. The reason for considering educational institutions is the severity of the educational needs and the high risk of rapid spread. Educational institutions offer an environment where students come from different regions and communicate with each other at close distances. To slow down the virus's spread rate, a method is proposed in this paper that differs from vaccinating the entire population or complete lockdown. In the present research, we identified a few key spreaders, which can be isolated and can slow down the transmission rate of the contagion. The present study creates a student communication network, and virus transmission is modeled over the predicted network. Using student-to-student communication data, three distinct networks are generated to analyze the roles of nodes responsible for the spread of this contagion. Intra-class and inter-class networks are generated, and the contagion spread was observed on them. Using social network strategies, we can decrease the maximum number of infections from 200 to 70 individuals, with contagion lasting in the network for 60 days.

Gauging urban resilience in the United States during the COVID-19 pandemic via social network analysis.

Social distancing policies and other restrictive measures have demonstrated efficacy in curbing the spread of the COVID-19 pandemic. However, these interventions have concurrently led to short- and long-term alterations in social connectedness. Comprehending the transformation in intracity social interactions is imperative for facilitating post-pandemic recovery and development. In this research, we employ social network analysis (SNA) to delve into the nuances of urban resilience. Specifically, we constructed intricate networks utilizing human mobility data to represent the impact of social interactions on the structural attributes of social networks while assessing urban resilience by examining the stability features of social connectedness. Our findings disclose a diverse array of responses to social distancing policies regarding social connectedness and varied social reactions across U.S. Metropolitan Statistical Areas (MSAs). Social networks generally exhibited a shift from dense to sparse configurations during restrictive orders, followed by a transition from sparse to dense arrangements upon relaxation of said orders. Furthermore, we analyzed the alterations in social connectedness as demonstrated by network centrality, which can presumably be attributed to the rigidity of policies and the inherent qualities of the examined MSAs. Our findings contribute valuable scientific insights to support informed decision-making for post-pandemic recovery and development initiatives.

[Keyword Network Analysis and Topic Modeling of News Articles Related to Artificial Intelligence and Nursing].

PURPOSE: The purpose of this study was to identify the main keywords, network properties, and main topics of news articles related to artificial intelligence technology in the field of nursing. METHODS: After collecting artificial intelligence-and nursing-related news articles published between January 1, 1991, and July 24, 2022, keywords were extracted via preprocessing. A total of 3,267 articles were searched, and 2,996 were used for the final analysis. Text network analysis and topic modeling were performed using NetMiner 4.4. RESULTS: As a result of analyzing the frequency of appearance, the keywords used most frequently were education, medical robot, telecom, dementia, and the older adults living alone. Keyword network analysis revealed the following results: a density of 0.002, an average degree of 8.79, and an average distance of 2.43; the central keywords identified were 'education,' 'medical robot,' and 'fourth industry.' Five topics were derived from news articles related to artificial intelligence and nursing: 'Artificial intelligence nursing research and development in the health and medical field,' 'Education using artificial intelligence for children and youth care,' 'Nursing robot for older adults care,' 'Community care policy and artificial intelligence,' and 'Smart care technology in an aging society.' CONCLUSION: The use of artificial intelligence may be helpful among the local community, older adult, children, and adolescents. In particular, health management using artificial intelligence is indispensable now that we are facing a super-aging society. In the future, studies on nursing intervention and development of nursing programs using artificial intelligence should be conducted.

Agri-food Cooperatives' Online Marketing: Evaluation of the Strategies Utilized by Spanish and UK Food Retailers Pre- and Post- COVID-19 Pandemic

El uso del Big Data por las grandes cadenas de alimentación está aumentando su poder de negociación frente al sector cooperativo productor agroalimentario. Este trabajo tiene como objetivo determinar el comportamiento en las redes sociales de los minoristas de alimentos que operan en España y el Reino Unido en las redes sociales, así como identificar cambios significativos antes y después de la pandemia de COVID-19. El estudio analiza los datos de Twitter de 16 minoristas de alimentos de los que se extrajo un total de 102.200 tweets válidos de sus cuentas oficiales. El análisis de contenido y de redes sociales mostró diferencias tanto en el comportamiento en Twitter de los supermercados del Reino Unido y de España, así como antes y durante la pandemia de COVID-19. Para las cooperativas agroalimentarias con poco poder de negociación en la cadena de suministro de productos frescos, el análisis de datos de redes sociales en internet es un factor clave para mejorar su posición competitiva. Estos hallazgos deberían ser valiosos para los científicos de datos y gerentes responsables de la formación de estrategias de las empresas agroalimentarias que tienen como clientes a grandes cadenas de alimentación. Finalmente, el estudio también confirma que, para las cooperativas agroalimentarias, el análisis de contenido de los tweets es una herramienta barata y útil para entender el comportamiento de sus clientes.Alternate :The use of big data by large food retailers is increasing their bargaining power against the agri-food cooperative sector. The aim of this study was to determine the social media behaviour of food retailers in Spain and the UK, and to identify significant changes pre- and post-COVID-19 pandemic. The study analysed Twitter data collected from 16 food retailers;a total of 102,200 valid tweets were extracted from their official Twitter accounts. A term frequency analysis and a social network analysis of food retailers' Twitter behaviour were carried out. The results obtained show differences for both UK and Spanish retailers before and during the COVID-19 pandemic. For agri-food cooperatives with little bargaining power in the supply chain of fresh produce, data analysis is a key factor in improving their competitive positioning. These findings should be of value to data scientists as well as managers responsible for forming strategies in agri-food firms that have large food retailers as clients. Finally, the study also confirms that, for agri-food cooperatives, analysing tweet content is a cheap and useful tool for understanding customer behaviour.

Opening the black box of Library Hi Tech: a social network and bibliometric analysis

Purpose: This paper presents a comprehensive study using bibliometric and social network analysis (SNA) to depict the academic community, research hotspots and the correlation between research performance and social network measurements within Library Hi Tech. Design/methodology/approach: Publications from Library Hi Tech between 2010 and 2022 are reviewed and analysed through coauthorship analysis, co-occurrence analysis, SNA and the Spearman rank correlation test. Findings: The annual number of publications in Library Hi Tech increased from 2016 to 2022, indicating that this research has gradually gained global attention. The USA and China are the most significant contributors to the relevant publications. Scholars in this field mainly engage in small-scale cooperation. Academic libraries, digital libraries, libraries, information technology and COVID-19 were hot topics during the study period. In light of the COVID-19 pandemic, there was a marked increase in research on healthcare. Academic interest in the internet of Things and social media has proliferated recently and may soon attract more attention. Spearman rank correlation analysis shows that research performance (i.e. publication count and citation count) is significantly and positively correlated with social network measurements (i.e. degree centrality, betweenness centrality, closeness centrality and eigenvector centrality) in studies of Library Hi Tech. Originality/value: This paper reveals a systematic picture of the research landscape of Library Hi Tech and provides a potential guide for future research. The relationship between scientific research performance and social network measurements can be objectively identified based on statistical knowledge. © 2023, Emerald Publishing Limited.

Public Opinion Mining on Construction Health and Safety: Latent Dirichlet Allocation Approach

The construction industry has been experiencing many occupational accidents as working on construction sites is dangerous. To reduce the likelihood of accidents, construction companies share the latest construction health and safety news and information on social media. While research studies in recent years have explored the perceptions towards these companies' social media pages, there are no big data analytic studies conducted on Instagram about construction health and safety. This study aims to consolidate public perceptions of construction health and safety by analyzing Instagram posts. The study adopted a big data analytics approach involving visual, content, user, and sentiment analyses of Instagram posts (n = 17,835). The study adopted the Latent Dirichlet Allocation, a kind of machine learning approach for generative probabilistic topic extraction, and the five most mentioned topics were: (a) training service, (b) team management, (c) training organization, (d) workers' work and family, and (e) users' action. Besides, the Jaccard coefficient co-occurrence cluster analysis revealed: (a) the most mentioned collocations were ‘construction safety week', ‘safety first', and ‘construction team', (b) the largest clusters were ‘safety training', ‘occupational health and safety administration', and ‘health and safety environment', (c) the most active users were ‘Parallel Consultancy Ltd.', ‘Pike Consulting Group', and ‘Global Training Canada', and (d) positive sentiment accounted for an overwhelming figure of 85%. The findings inform the industry on public perceptions that help create awareness and develop preventative measures for increased health and safety and decreased incidents.

An Integrated Neutrosophic SNA-MABAC Approach for Blockchain Applicability Evaluation in Sustainable Supply Chains

The outbreak of the novel coronavirus pneumonia and the turbulent international situation in recent years have seriously disrupted the normal operation of the entire supply chain (SC). As an emerging technology, blockchain is characterized by decentralization, reliability, transparency and traceability, which can be effectively applied to solve social, environmental and economic concerns and achieve sustainability of supply chain. However, whether blockchain is suitable for every function of a sustainable supply chain (SSC), or what function is best suited for the application of a set of blockchain criteria, can be viewed as a multi-criteria group decision-making (MCGDM) problem. This paper presents a combined MCGDM technique utilizing the social network analysis (SNA) and Multi-Attributive Border Approximation Area Comparison (MABAC), for selecting an appropriate function of SSCs to implement blockchain technology with Neutrosophic information. The framework gives quantitative consideration to the weight of relevant blockchain criteria and decision makers under high uncertainty. This study can also facilitate the effective allocation of resources and enhance the competitiveness of SSCs in the coordinated planning of various blockchain deployments. © 2022 IEEE.

Learning Analytics Overview: Academic Approach and Machine Learning Possibilities

In the Covid-19 era, traditional lecture-based teaching has been undergoing changes in learning design, learners' engagement, and technology integration. Online learning has become an integral part of education around the globe due to its flexibility in learning with respect to place and time. These online courses are available to larger audiences and enable students to have more freedom over the study process. However, freedom also means that instructors have less control to keep students making progress on the course. The flexibility of online courses is encouraging the students to enrol with a few clicks but most of these students are dropping out due to losing interest in the course contents within a few weeks. On the contrary, online learning produces large amounts of data that can be used to follow the learning process and give useful insights for both teachers and students. Learning analytics algorithms utilize these data to identify the factors and parameters that can explain learners' dropout rate, learning performance, as well as suggest possible actions to intrigue learners' active engagement. To conduct further research on the parameters affecting learners' participation in an online course, it is essential to find out the previous research works, best practices, and research trends of learning analytics. In the scope of this work, the authors formulated a search query to generate a pool of most relevant papers from the Scopus database and, hence, identified seven clusters which illustrate the landscape of the learning analytics domain. The authors also employed the Latent Dirichlet allocation (LDA) topic modeling algorithm which is a form of text data mining and statistical machine learning approach to compare the similarities with the clusters generated as a part of literature review. The authors further analyzed the papers in each cluster to identify the parameters which are significant to build a predictive model on learners' dropout rate. This work not only provides a baseline to conduct further research to find out the parameters affecting learners' retention rate but also introduces a systematic methodology to validate the findings of the literature review with a data-driven algorithmic approach. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Measuring the Interference Effect of Bots in Disseminating Opposing Viewpoints Related to COVID-19 on Twitter Using Epidemiological Modeling

The activity of bots can influence the opinions and behavior of people, especially within the political landscape where hot-button issues are debated. To evaluate the bot presence among the propagation trends of opposing politically-charged viewpoints on Twitter, we collected a comprehensive set of hashtags related to COVID-19. We then applied both the SIR (Susceptible, Infected, Recovered) and the SEIZ (Susceptible, Exposed, Infected, Skeptics) epidemiological models to three different dataset states including, total tweets in a dataset, tweets by bots, and tweets by humans. Our results show the ability of both models to model the diffusion of opposing viewpoints on Twitter, with the SEIZ model outperforming the SIR. Additionally, although our results show that both models can model the diffusion of information spread by bots with some difficulty, the SEIZ model outperforms. Our analysis also reveals that the magnitude of the bot-induced diffusion of this type of information varies by subject. © 2023 IEEE Computer Society. All rights reserved.

The role of social networks and economic capital in familial coping during lockdowns

This study examines the coping capacities of families during the Covid-19 lockdown in relation to their material and social resources. Specifically, we examine (1) how familial modes of living changed during the lockdown;(2) how the affected families coped with these changes;and (3) how families' coping strategies were influenced by their social and material capital. We analyze problem centered interviews with 30 families and the ego-centered networks of these families, conducted in spring 2020 and autumn 2021. Our typology shows that inequalities increased in the pandemic. In families that had both strong networks and ample material security, the crisis had only a minor impact on how they lived their everyday lives. By contrast, families that had neither supportive networks nor sufficient material resources felt vulnerable during the crisis, and threatened to break down. Families that had either support from their networks or greater material security were able to cope only with great effort. © 2023 Nomos Verlagsgesellschaft mbH und Co. All rights reserved.