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2.
J Urol ; 205(1): 290-292, 2021 01.
Article in English | MEDLINE | ID: covidwho-20242624
3.
Cien Saude Colet ; 26(9): 4065-4068, 2021 Sep.
Article in Portuguese, English | MEDLINE | ID: covidwho-20240486

ABSTRACT

This paper highlights the advance of science in interpreting pandemics, in contrast to the failure of governments that politicized the approach to the global public health emergency resulting from the COVID-19 pandemic. This study reflects on cognitive dissonance caused by the infodemic. It addresses the need to apply infodemiology to mitigate the deleterious effects of fake news intentionally fabricated to confuse, mislead, manipulate, and deny the reality without losing sight of the fact that the roots of the problem are historical, circumstantial, profound, and challenging. This work reveals the impacts of this situation for health professionals and exposes the fine line between freedom of expression and the fundamental right to life, leading to the conclusion that wrong choices in public health can cause preventable deaths.


O artigo evidencia o avanço da ciência na interpretação de pandemias, em contraste com o fracasso de governos que politizaram a abordagem da emergência de saúde pública global decorrente da COVID-19. Trata-se de um estudo que apresenta uma reflexão sobre o processo de dissonância cognitiva causada pela infodemia e aborda a necessidade de aplicar a infodemiologia para mitigar os efeitos deletérios de notícias falsas que são fabricadas intencionalmente, com o objetivo de confundir, enganar, manipular e negar a realidade, sem, contudo, perder de vista que as raízes do problema são históricas, conjunturais, profundas e de difícil solução. O trabalho revela os impactos dessa situação para profissionais de saúde e expõe a linha tênue que existe entre a liberdade de expressão e o direito essencial à vida, levando à conclusão de que escolhas erradas, no que tange à saúde pública, podem causar mortes evitáveis.


Subject(s)
COVID-19 , Social Media , Humans , Pandemics/prevention & control , Public Health , SARS-CoV-2
4.
Cien Saude Colet ; 26(suppl 3): 4810, 2021 11 15.
Article in English, Portuguese | MEDLINE | ID: covidwho-20240485
7.
Int J Environ Res Public Health ; 20(10)2023 05 12.
Article in English | MEDLINE | ID: covidwho-20241601

ABSTRACT

Popular social media platforms, such as Twitter, have become an excellent source of information with their swift information dissemination. Individuals with different backgrounds convey their opinions through social media platforms. Consequently, these platforms have become a profound instrument for collecting enormous datasets. We believe that compiling, organizing, exploring, and analyzing data from social media platforms, such as Twitter, can offer various perspectives to public health organizations and decision makers in identifying factors that contribute to vaccine hesitancy. In this study, public tweets were downloaded daily from Tweeter using the Tweeter API. Before performing computation, the tweets were preprocessed and labeled. Vocabulary normalization was based on stemming and lemmatization. The NRCLexicon technique was deployed to convert the tweets into ten classes: positive sentiment, negative sentiment, and eight basic emotions (joy, trust, fear, surprise, anticipation, anger, disgust, and sadness). t-test was used to check the statistical significance of the relationships among the basic emotions. Our analysis shows that the p-values of joy-sadness, trust-disgust, fear-anger, surprise-anticipation, and negative-positive relations are close to zero. Finally, neural network architectures, including 1DCNN, LSTM, Multiple-Layer Perceptron, and BERT, were trained and tested in a COVID-19 multi-classification of sentiments and emotions (positive, negative, joy, sadness, trust, disgust, fear, anger, surprise, and anticipation). Our experiment attained an accuracy of 88.6% for 1DCNN at 1744 s, 89.93% accuracy for LSTM at 27,597 s, while MLP achieved an accuracy of 84.78% at 203 s. The study results show that the BERT model performed the best, with an accuracy of 96.71% at 8429 s.


Subject(s)
COVID-19 , Social Media , Humans , Sentiment Analysis , COVID-19 Vaccines , Public Health , COVID-19/prevention & control , Data Mining , Neural Networks, Computer , Vaccination
8.
J Med Internet Res ; 25: e44356, 2023 Jun 09.
Article in English | MEDLINE | ID: covidwho-20240023

ABSTRACT

BACKGROUND: Digital misinformation, primarily on social media, has led to harmful and costly beliefs in the general population. Notably, these beliefs have resulted in public health crises to the detriment of governments worldwide and their citizens. However, public health officials need access to a comprehensive system capable of mining and analyzing large volumes of social media data in real time. OBJECTIVE: This study aimed to design and develop a big data pipeline and ecosystem (UbiLab Misinformation Analysis System [U-MAS]) to identify and analyze false or misleading information disseminated via social media on a certain topic or set of related topics. METHODS: U-MAS is a platform-independent ecosystem developed in Python that leverages the Twitter V2 application programming interface and the Elastic Stack. The U-MAS expert system has 5 major components: data extraction framework, latent Dirichlet allocation (LDA) topic model, sentiment analyzer, misinformation classification model, and Elastic Cloud deployment (indexing of data and visualizations). The data extraction framework queries the data through the Twitter V2 application programming interface, with queries identified by public health experts. The LDA topic model, sentiment analyzer, and misinformation classification model are independently trained using a small, expert-validated subset of the extracted data. These models are then incorporated into U-MAS to analyze and classify the remaining data. Finally, the analyzed data are loaded into an index in the Elastic Cloud deployment and can then be presented on dashboards with advanced visualizations and analytics pertinent to infodemiology and infoveillance analysis. RESULTS: U-MAS performed efficiently and accurately. Independent investigators have successfully used the system to extract significant insights into a fluoride-related health misinformation use case (2016 to 2021). The system is currently used for a vaccine hesitancy use case (2007 to 2022) and a heat wave-related illnesses use case (2011 to 2022). Each component in the system for the fluoride misinformation use case performed as expected. The data extraction framework handles large amounts of data within short periods. The LDA topic models achieved relatively high coherence values (0.54), and the predicted topics were accurate and befitting to the data. The sentiment analyzer performed at a correlation coefficient of 0.72 but could be improved in further iterations. The misinformation classifier attained a satisfactory correlation coefficient of 0.82 against expert-validated data. Moreover, the output dashboard and analytics hosted on the Elastic Cloud deployment are intuitive for researchers without a technical background and comprehensive in their visualization and analytics capabilities. In fact, the investigators of the fluoride misinformation use case have successfully used the system to extract interesting and important insights into public health, which have been published separately. CONCLUSIONS: The novel U-MAS pipeline has the potential to detect and analyze misleading information related to a particular topic or set of related topics.


Subject(s)
COVID-19 , Social Media , Humans , Big Data , Artificial Intelligence , Ecosystem , Fluorides , Communication
9.
BMC Public Health ; 23(1): 1069, 2023 06 05.
Article in English | MEDLINE | ID: covidwho-20239868

ABSTRACT

BACKGROUND: COVID-19 has triggered a global public health crisis, and had an impact on economies, societies, and politics around the world. Based on the pathogen prevalence hypothesis suggested that residents of areas with higher infection rates are more likely to be collectivists as compared with those of areas with lower infection rates. Many researchers had studied the direct link between infectious diseases and individualism/collectivism (infectious diseases→ cultural values), but no one has focused on the specific psychological factors between them: (infectious diseases→ cognition of the pandemic→ cultural values). To test and develop the pathogen prevalence hypothesis, we introduced pandemic mental cognition and conducted an empirical study on social media (Chinese Sina Weibo), hoping to explore the psychological reasons behind in cultural value changes in the context of a pandemic. METHODS: We downloaded all posts from active Sina Weibo users in Dalian during the pandemic period (January 2020 to May 2022) and used dictionary-based approaches to calculate frequency of words from two domains (pandemic mental cognition and collectivism/individualism), respectively. Then we used the multiple log-linear regression analysis method to establish the relationship between pandemic mental cognition and collectivism/individualism. RESULTS: Among three dimensions of pandemic mental cognition, only the sense of uncertainty had a significant positive relationship with collectivism, and also had a marginal significant positive relationship with individualism. There was a significant positive correlation between the first-order lag term AR(1) and individualism, which means the individualism tendency was mainly affected by its previous level. CONCLUSIONS: The study found that more collectivist regions are associated with a higher pathogen burden, and recognized the sense of uncertainty as its underlying cause. Results of this study validated and further developed the pathogen stress hypothesis in the context of the COVID-19 pandemic.


Subject(s)
COVID-19 , Communicable Diseases , Social Media , Humans , COVID-19/epidemiology , Pandemics , Cognition , Communicable Diseases/epidemiology
10.
Arch Ital Urol Androl ; 95(2): 11341, 2023 May 29.
Article in English | MEDLINE | ID: covidwho-20239808

ABSTRACT

OBJECTIVE: To assess the quality content of YouTubeTM videos on telemedicine during COVID-19 pandemic. MATERIALS AND METHODS: First, the frequency of worldwide YouTube™ and Google™ searches for telemedicine was analyzed. Second, we queried YouTube™ with telemedicine-related terms. Third, the Patient Education Materials Assessment Tool for Audiovisual Materials (PEMAT A/V), the Global Quality Score (GQS), and the Misinformation tool were used for the quality assessment. RESULTS: According to selection criteria, 129 videos were collected for the analysis. From January 2018 to January 2022, the peak relative interest on YouTube™ and Google™ occurred in March 2020. Of all, 27.1 and 72.9% were uploaded before (Jan 2018-Feb 2020) and after (Mar 2020-Mar 2022) the COVID-19 outbreak, respectively. According to the PEMAT A/V, the overall median understandability and actionability was 50.0% (33.3 [IQR 0-66.7] vs 50.0 [27.1-75], p = 0.2) and 66.7% (63.6 [IQR 50.0-75.7] vs 67.9 [50.0-79.2],p = 0.6), respectively. According to GQS, 3.9%, 17.8%, 24.0%, 26.4% and 27.9% were classified as excellent, good, medium, generally poor, and poor-quality videos, respectively. The highest rate of poor-quality videos was recorded in videos uploaded before COVID-19 pandemic (37.1 vs 24.5%). According to overall misinformation score, a higher score was recorded for the videos uploaded after COVID-19 pandemic (1.8 [IQR 1.4-2.3] vs 2.2 [1.8-2.8], p = 0.01). CONCLUSIONS: The interest in telemedicine showed a significant peak when the COVID-19 pandemic was declared. However, the contents provided on YouTubeTM were not informative enough. In the future, official medical institutions should standardize telemedicine regulation and online content to reduce the widespread of misleading information.


Subject(s)
COVID-19 , Social Media , Humans , COVID-19/epidemiology , Pandemics , Video Recording
11.
Int J Environ Res Public Health ; 20(11)2023 May 27.
Article in English | MEDLINE | ID: covidwho-20239567

ABSTRACT

Loneliness has been linked to morbidity and mortality across the lifespan. Social media could reduce loneliness, though research on the relation between social media and loneliness has been inconclusive. This study used person-centered analyses to elucidate the inconsistencies in the literature and examine the possible role technology barriers played in the relation between social media use and loneliness during the COVID-19 pandemic. Participants (n = 929; M age = 57.58 ± 17.33) responded to a series of online questions covering demographics, loneliness, technology barriers, and social media use (e.g., Facebook, Twitter, etc.) across a range of devices (e.g., computer, smartphone, etc.). A latent profile analysis was conducted to identify distinct profiles of social media use, loneliness patterns, and age. Results yielded five distinct profiles characterized that showed no systematic associations among age, social media use, and loneliness. Demographic characteristics and technology barriers also differed between profiles and were associated with loneliness. In conclusion, person-centered analyses demonstrated distinct groups of older and younger adults that differed on social media use and loneliness and may offer more fruitful insights over variable-centered approaches (e.g., regression/correlation). Technology barriers may be a viable target for reducing loneliness in adults.


Subject(s)
COVID-19 , Social Media , Humans , Adult , Middle Aged , Aged , COVID-19/epidemiology , Loneliness , Pandemics , Fruit , Social Isolation
12.
J Med Internet Res ; 25: e39484, 2023 06 12.
Article in English | MEDLINE | ID: covidwho-20238400

ABSTRACT

BACKGROUND: Twitter has become a dominant source of public health data and a widely used method to investigate and understand public health-related issues internationally. By leveraging big data methodologies to mine Twitter for health-related data at the individual and community levels, scientists can use the data as a rapid and less expensive source for both epidemiological surveillance and studies on human behavior. However, limited reviews have focused on novel applications of language analyses that examine human health and behavior and the surveillance of several emerging diseases, chronic conditions, and risky behaviors. OBJECTIVE: The primary focus of this scoping review was to provide a comprehensive overview of relevant studies that have used Twitter as a data source in public health research to analyze users' tweets to identify and understand physical and mental health conditions and remotely monitor the leading causes of mortality related to emerging disease epidemics, chronic diseases, and risk behaviors. METHODS: A literature search strategy following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extended guidelines for scoping reviews was used to search specific keywords on Twitter and public health on 5 databases: Web of Science, PubMed, CINAHL, PsycINFO, and Google Scholar. We reviewed the literature comprising peer-reviewed empirical research articles that included original research published in English-language journals between 2008 and 2021. Key information on Twitter data being leveraged for analyzing user language to study physical and mental health and public health surveillance was extracted. RESULTS: A total of 38 articles that focused primarily on Twitter as a data source met the inclusion criteria for review. In total, two themes emerged from the literature: (1) language analysis to identify health threats and physical and mental health understandings about people and societies and (2) public health surveillance related to leading causes of mortality, primarily representing 3 categories (ie, respiratory infections, cardiovascular disease, and COVID-19). The findings suggest that Twitter language data can be mined to detect mental health conditions, disease surveillance, and death rates; identify heart-related content; show how health-related information is shared and discussed; and provide access to users' opinions and feelings. CONCLUSIONS: Twitter analysis shows promise in the field of public health communication and surveillance. It may be essential to use Twitter to supplement more conventional public health surveillance approaches. Twitter can potentially fortify researchers' ability to collect data in a timely way and improve the early identification of potential health threats. Twitter can also help identify subtle signals in language for understanding physical and mental health conditions.


Subject(s)
COVID-19 , Health Communication , Social Media , Humans , Linguistics , Public Health
13.
Lancet ; 401(10390): 1757, 2023 05 27.
Article in English | MEDLINE | ID: covidwho-20237269
14.
BMC Psychiatry ; 23(1): 371, 2023 05 26.
Article in English | MEDLINE | ID: covidwho-20236246

ABSTRACT

AIM: To investigate the relationship between social media use and loneliness and psychological wellbeing of youth in rural New South Wales. DESIGN: This was a web-based cross-sectional survey. METHODS: The survey consisted of 33 items including demography (12 items), participants' social media use (9 items), mood and anxiety (6 items), perceived loneliness (6 items), the impact of COVID-19 on social media usage or perceived loneliness (2 items). The participants' mood and anxiety were evaluated using the psychological distress tool (K6), while loneliness was measured using the De Jong Gierveld 6-item scale. Total loneliness and psychological distress scores were compared between demographic variables. RESULTS: A total of 47 participants, aged 16-24 years took part in the study. The majority were women (68%) and many had K6 score that was indicative of psychological distress (68%). About half of the participants indicated that Facebook (FB) was their most used social media platform and two in five participants were on social media within 10 min of waking up each day, about 30% spent more than 20 h per week on social media, and more than two-third sent private messages, images, or videos, multiple times a day. The mean loneliness score was 2.89 (range, 0 to 6), with 0 being 'not lonely' and 6 being 'intense social loneliness'. One-way ANOVA and χ2 test results showed that those who used FB most frequently had significantly higher mean scores for loneliness compared to those that used other social media platforms (p = 0.015). Linear regression analysis revealed that those who commonly used FB were more likely to report higher loneliness scores (coefficient = -1.45, 95%CI -2.63, -0.28, p = 0.017), while gender (p = 0.039), age (p = 0.048), household composition (p = 0.023), and education level (p = 0.014) were associated with severe psychological distress. CONCLUSIONS: The study found that social media usage, particularly FB, as measured by time used and active or passive engagement with the medium, was significantly linked to loneliness, with some impact on psychological distress. Social media use within ten minutes of waking increased the likelihood of psychological distress. However, neither loneliness nor psychological distress were associated with rurality among the rural youth in this study.


Subject(s)
COVID-19 , Social Media , Humans , Male , Female , Adolescent , Loneliness/psychology , Cross-Sectional Studies , Pilot Projects
15.
Nutrients ; 15(10)2023 May 16.
Article in English | MEDLINE | ID: covidwho-20234826

ABSTRACT

Social media is a popular source of nutrition information and can influence food choice. Instagram is widely used in Australia, and nutrition is frequently discussed on Instagram. However, little is known about the content of nutrition information published on Instagram. The aim of this study was to examine the content of nutrition-related posts from popular Australian Instagram accounts. Australian Instagram accounts with ≥100,000 followers, that primarily posted about nutrition, were identified. All posts from included accounts, from September 2020 to September 2021, were extracted and posts about nutrition were included. Post captions were analysed using Leximancer, a content analysis software, to identify concepts and themes. Text from each theme was read to develop a description and select illustrative quotes. The final sample included 10,964 posts from 61 accounts. Five themes were identified: (1) recipes; (2) food and nutrition practices; (3) body goals; (4) food literacy and (5) cooking at home. Recipes and practical information about nutrition and food preparation are popular on Instagram. Content about weight loss and physique-related goals is also popular and nutrition-related Instagram posts frequently include marketing of supplements, food and online programs. The popularity of nutrition-related content indicates that Instagram may be a useful health-promotion setting.


Subject(s)
Marketing , Social Media , Humans , Australia , Health Promotion , Food , Dietary Supplements , Nutritive Value
17.
BMJ Open ; 13(6): e066897, 2023 06 06.
Article in English | MEDLINE | ID: covidwho-20233982

ABSTRACT

OBJECTIVES: To (1) understand what behaviours, beliefs, demographics and structural factors predict US adults' intention to get a COVID-19 vaccination, (2) identify segments of the population ('personas') who share similar factors predicting vaccination intention, (3) create a 'typing tool' to predict which persona people belong to and (4) track changes in the distribution of personas over time and across the USA. DESIGN: Three surveys: two on a probability-based household panel (NORC's AmeriSpeak) and one on Facebook. SETTING: The first two surveys were conducted in January 2021 and March 2021 when the COVID-19 vaccine had just been made available in the USA. The Facebook survey ran from May 2021 to February 2022. PARTICIPANTS: All participants were aged 18+ and living in the USA. OUTCOME MEASURES: In our predictive model, the outcome variable was self-reported vaccination intention (0-10 scale). In our typing tool model, the outcome variable was the five personas identified by our clustering algorithm. RESULTS: Only 1% of variation in vaccination intention was explained by demographics, with about 70% explained by psychobehavioural factors. We identified five personas with distinct psychobehavioural profiles: COVID Sceptics (believe at least two COVID-19 conspiracy theories), System Distrusters (believe people of their race/ethnicity do not receive fair healthcare treatment), Cost Anxious (concerns about time and finances), Watchful (prefer to wait and see) and Enthusiasts (want to get vaccinated as soon as possible). The distribution of personas varies at the state level. Over time, we saw an increase in the proportion of personas who are less willing to get vaccinated. CONCLUSIONS: Psychobehavioural segmentation allows us to identify why people are unvaccinated, not just who is unvaccinated. It can help practitioners tailor the right intervention to the right person at the right time to optimally influence behaviour.


Subject(s)
COVID-19 , Social Media , Adult , Humans , United States/epidemiology , COVID-19 Vaccines/therapeutic use , COVID-19/epidemiology , COVID-19/prevention & control , Self Report , Intention , Probability , Vaccination
18.
Sci Rep ; 13(1): 9245, 2023 06 07.
Article in English | MEDLINE | ID: covidwho-20233827

ABSTRACT

This article uses novel data collected on a weekly basis covering more than 35,000 individuals in the EU to analyze the relationship between trust in various dimensions and COVID-19 vaccine hesitancy. We found that trust in science is negatively correlated, while trust in social media and the use of social media as the main source of information are positively associated with vaccine hesitancy. High trust in social media is found among adults aged 65+, financially distressed and unemployed individuals, and hesitancy is largely explained by conspiracy beliefs among them. Finally, we found that the temporary suspension of the AstraZeneca vaccine in March 2021 significantly increased vaccine hesitancy and especially among people with low trust in science, living in rural areas, females, and financially distressed. Our findings suggest that trust is a key determinant of vaccine hesitancy and that pro-vaccine campaigns could be successfully targeted toward groups at high risk of hesitancy.


Subject(s)
COVID-19 , Social Media , Female , Humans , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19 Vaccines , Emotions , Trust , Aged , Male
19.
Environ Sci Pollut Res Int ; 30(33): 79960-79979, 2023 Jul.
Article in English | MEDLINE | ID: covidwho-20232117

ABSTRACT

After COVID-19, financing for emerging nation reserves in renewable energy bases was deemed a crucial aspect of sustainable development. Investing in biogas energy plants can be highly beneficial for lowering the use of fossil fuels. Using a survey of shareholders, investors, biogas energy professionals, and active social media participants in Pakistan, this study evaluates the intentions of individual investors to invest in biogas energy plants. The primary purpose of this study is to increase investment intent for biogas energy projects following COVID-19. This study focuses on financing biogas energy plants in the post-COVID-19 era and evaluates the research's assumptions using partial least squares structural equation modeling (PLS-SEM). The study employed the technique of purposive sampling to acquire data for this investigation. The results indicate that attitudes, perceived biogas energy benefits, perceived investment attitudes, and supervisory structure evaluations inspire one's propensity to finance biogas vitality plant efforts. The study found a link between eco-friendly responsiveness, monetary benefits, and investors' actions. The aspiration of investors to mark such reserves was set up to be unpretentious by their risk aversion. Conferring to the facts, evaluating the monitoring structure is the critical factor. The previous studies on investment behavior and other forms of pro-environmental intent and action yielded contradictory results. In addition, the regulatory environment was evaluated to see how the theory of planned behavior (TPB) affects financiers' objectives to participate in biogas power plants. The consequences of the study indicate that feelings of pride and discernment of energy expansively affect people's desire to invest in biogas plants. Biogas energy efficacy has little effect on investors' decisions to invest in biogas energy plants. This study offers policymakers practical ideas on enhancing investments in biogas energy plants.


Subject(s)
COVID-19 , Social Media , Humans , Biofuels , Climate Change , Economic Development , Inventions , Pandemics
20.
J Psychosoc Nurs Ment Health Serv ; 61(6): 33-42, 2023 Jun.
Article in English | MEDLINE | ID: covidwho-20245338

ABSTRACT

The current study aimed to analyze whether individuals with problematic social media use (PSMU) demonstrate attentional bias (AB) toward negative emotional information and determine the relationships among the severity of PSMU, social anxiety, and negative AB. Sixty participants were divided into problematic and normal use groups according to their scores on the Bergen Social Media Addiction Scale (BSMAS). The BSMAS and Interaction Anxiety Scale were adopted to measure the severity of PSMU and social anxiety, respectively. An emotional Stroop task and a visual dot-probe task (DPT) were used to assess AB toward negative emotional expressions. Relationships among the severity of PSMU, social anxiety, and negative AB were investigated using Pearson's correlation coefficient. Results showed that individuals with PSMU demonstrated AB toward negative emotional information in the emotional DPT but not in the emotional Stroop task. AB toward negative emotional information was positively correlated with the severity of PSMU and social anxiety in the emotional DPT. Findings support the key role of negative AB and social anxiety in individuals with PSMU, suggesting that more attention be paid to negative AB and social anxiety for the prevention and treatment of PSMU. [Journal of Psychosocial Nursing and Mental Health Services, 61(6), 33-42.].


Subject(s)
Attentional Bias , Social Media , Humans , Emotions , Anxiety/psychology
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