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We analyze a dataset from Twitter of misinformation related to the COVID-19 pandemic. We consider this dataset from the intersection of two important but, heretofore, largely separate perspectives: misinformation and trust. We apply existing direct trust measures to the dataset to understand their topology, and to better understand if and how trust relates to spread of misinformation online. We find evidence for small worldness in the misinformation trust network;outsized influence from broker nodes;a digital fingerprint that may indicate when a misinformation trust network is forming;and, a positive relationship between greater trust and spread of misinformation. © 2022 IEEE.
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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.
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Heightened racial tensions during the COVID-19 pandemic contributed to the increase and rapid propagation of online hate speech towards Asians. In this work, we study the relationship between the racist narratives and conspiracy theories that emerged related to COVID-19 and historical stereotypes underpinning Asian hate and counter-hate speech on Twitter, in particular the Yellow Peril and model minority tropes. We find that the pandemic catalyzed a broad increase in discourse engaging with racist stereotypes extending beyond COVID-19 specifically. We also find that racist narratives and conspiracy theories which emerged during the pandemic and gained widespread attention were rooted in deeply-embedded Asian stereotypes. In alignment with theories of idea habitat and processing fluency, our work suggests that historical stereotypes provided an environment vulnerable to the racist narratives and conspiracy theories which emerged during the pandemic. Our work offers insight for ongoing and future anti-racist efforts. © 2023 ACM.
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Aim. An online survey among social network users was conducted to assess the frequency of COVID-19 cases, the spectrum of medications used for treatment, and the subjective assessment of clinical manifestations of the disease. Material and methods. An anonymous online survey was conducted among users of various social networks using a questionnaire created on the SurveyMonkey survey and research platform. During the first month of December 2021, the survey included 23 questions regarding the clinical and demographic characteristics of respondents, the number of COVID-19 cases, clinical manifestations, and severity, as well as the need for medical help and medication. Results. 752 respondents took part in the online survey, more than 70% of them are under 50 years old. Among the respondents 59.73% had a new coronavirus infection (COVID-19). More than 40% of the participants had COVID-19 in the period from September 2020 to April 2021 (2nd wave in Russia). In 79.2% of people, the presence of a new coronavirus infection was confirmed by one of the diagnostic methods: polymerase chain reaction (PCR test), radiography, the presence of antibodies to Ig G/M, and took into account the presence of contact with infected SARS-CoV-2. 411 participants observed any clinical manifestations of the disease. Most often respondents who had COVID-19 indicated weakness, cough, dyspnea, disappearance or decrease in the acuity of smell and taste. The volume of lung tissue damage in 36.5% of cases was less than 25%. The disappearance of any clinical manifestations of the disease immediately after recovery was noted by 32.0% of respondents. Most of the patients (59.2%) sought medical help at the polyclinic, 38.9% had to self-medicate. 71.9% respondents indicated they had been vaccinated against COVID-19, but without specifying the timing and completeness of the course. Side effects after immunization (fever, weakness, soreness, and redness at the injection site) were subjectively assessed by 41.9% of respondents. Conclusion. Among the surveyed respondents, 62.7% of the disease symptoms were mild. The highest number of cases occurred in the 2nd and 4th waves of COVID-19 morbidity in Russia. Most often respondents indicated symptoms of acute respiratory infection. The complete disappearance of clinical manifestations of the disease immediately after recovery was noted by 32.0 % of respondents, and the persistence of symptoms for up to a year - 7.5. More than 70% of the participants in the online survey reported vaccination against COVID-19, but the questionnaire did not include questions about the timing of vaccinations (before or after COVID-19) and the completeness of the course.Copyright © Eco-Vector, 2023. All rights reserved.
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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.
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Intro: Antimicrobial resistance has been considered a global health problem, being one of the 10 most important public health problems worldwide. During the COVID-19 pandemic, a large number of antibiotics were used without any control, both on an outpatient basis and in hospitalization, which can have serious consequences. Method(s): Prior informed consent, a cross-sectional descriptive study was conducted with a sample of 322 physicians. A survey was carried out in Google forms, and it was shared by telephone messages and social networks. Finding(s): 322 physicians, 60.9% women and 39.1% men, mean age 33.4 years, 85.9% general practitioners, 11.5% specialist physicians. 47.2% work in private institutions, 36% in public centers. Antibiotic policies in institutions: 50.3% do not have regulations for the use of antibiotics in COVID-19, only 29.5% work with policies to control the use of antibiotics and control measures against antibiotic resistance, 73.9% lack a committee for infection control and prevention of antibiotic resistance. Use of antibiotics: only 20.8% never used antibiotics in COVID-19, 45.6% used antibiotics in severe COVID-19, 40.4% hospitalized patients, and 33.5% outpatients. The most used antibiotics were azithromycin (67.1%), levofloxacin (53.4%), ceftriaxone (53.4%), and amoxicillin plus clavulanic acid (34.8%). 43.2% consider that using antibiotics can reduce mortality in moderate and severe COVID-19, 50.3% believe antibiotics should be suspended if there is clinical improvement, 96.3% believe there is an increase in self-medication of antibiotics, 59.8% consider that they used antibiotics inconrrectly at some point, 58.1% consider antibiotic resistance to be a problem in their workplace. 86.3% currently make less use of antibiotics in patients with COVID-19. Conclusion(s): In Honduras, there are deficiencies in policies for the use of antibiotics and control of antimicrobial resistance. Most doctors consider that they have used antibiotics incorrectly, including antibiotics that can generate resistance.Copyright © 2023
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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.
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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.
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BACKGROUND: We aimed to characterize the prevalence of social disconnection and thoughts of suicide among older adults in the United States, and examine the association between them in a large naturalistic study. METHODS: We analyzed data from 6 waves of a fifty-state non-probability survey among US adults conducted between February and December 2021. The internet-based survey collected the PHQ-9, as well as multiple measures of social connectedness. We applied multiple logistic regression to analyze the association between presence of thoughts of suicide and social disconnection. Exploratory analysis, using generalized random forests, examined heterogeneity of effects across sociodemographic groups. RESULTS: Of 16,164 survey respondents age 65 and older, mean age was 70.9 (SD 5.0); the cohort was 61.4 % female and 29.6 % male; 2.0 % Asian, 6.7 % Black, 2.2 % Hispanic, and 86.8 % White. A total of 1144 (7.1 %) reported thoughts of suicide at least several days in the prior 2 week period. In models adjusted for sociodemographic features, households with 3 or more additional members (adjusted OR 1.73, 95 % CI 1.28-2.33) and lack of social supports, particularly emotional supports (adjusted OR 2.60, 95 % CI 2.09-3.23), were independently associated with greater likelihood of reporting such thoughts, as was greater reported loneliness (adjusted OR 1.75, 95 % CI 1.64-1.87). The effects of emotional support varied significantly across sociodemographic groups. CONCLUSIONS: Thoughts of suicide are common among older adults in the US, and associated with lack of social support, but not with living alone. TRIAL REGISTRATION: NA.
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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.
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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.
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Artificial Intelligence , Nursing Research , Child , Humans , Aged , AdolescentABSTRACT
BACKGROUND: Since humans are social animals, social relations are incredibly important. However, in cases of contagious diseases such as the flu, social contacts also pose a health risk. According to prominent health behavior change theories, perceiving a risk for one's health motivates precautionary behaviors. The "behavioral immune system" approach suggests that social distancing might be triggered as a precautionary, evolutionarily learned behavior to prevent transmitting contagious diseases through social contact. This study examines the link between personal risk perception for an infectious disease and precautionary behavior for disease-prevention in the context of social relationships. METHODS: At 2-week intervals during the first semester, 100 Psychology freshmen indicated their flu risk perception, whether they had been ill during the previous week, and their friendships within their freshmen network for eight time points. RESULTS: Social network analysis revealed that participants who reported a high flu risk perception listed fewer friends (B = -0.10, OR = 0.91, p = 0.026), and were more likely to be ill at the next measuring point (B = 0.26, OR = 1.30, p = 0.005). Incoming friendship nominations increased the likelihood of illness (B = 0.14, OR = 1.15, p = 0.008), while the reduced number of friendship nominations only marginally decreased this likelihood (B = -0.07, OR = 0.93, p = 0.052). CONCLUSION: In accordance with the concept of a "behavioral immune system," participants with high flu risk perception displayed a social precautionary distancing even when in an environment, in which the behavior was ineffective to prevent an illness.