The infodemic transcends the pandemic. / A infodemia transcende a pandemia.

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.

The Role of Attentional Bias Toward Negative Emotional Information and Social Anxiety in Problematic Social Media Use.

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.].

Problematic use of the internet, smartphones, and social media among medical students and relationship with depression: An exploratory study.

BACKGROUND: Students in sub-Saharan African countries experienced online classes for the first time during the COVID-19 pandemic. For some individuals, greater online engagement can lead to online dependency, which can be associated with depression. The present study explored the association between problematic use of the internet, social media, and smartphones with depression symptoms among Ugandan medical students. METHODS: A pilot study was conducted among 269 medical students at a Ugandan public university. Using a survey, data were collected regarding socio-demographic factors, lifestyle, online use behaviors, smartphone addiction, social media addiction, and internet addiction. Hierarchical linear regression models were performed to explore the associations of different forms of online addiction with depression symptom severity. RESULTS: The findings indicated that 16.73% of the medical students had moderate to severe depression symptoms. The prevalence of being at risk of (i) smartphone addiction was 45.72%, (ii) social media addiction was 74.34%, and (iii) internet addiction use was 8.55%. Online use behaviors (e.g., average hours spent online, types of social media platforms used, the purpose for internet use) and online-related addictions (to smartphones, social media, and the internet) predicted approximately 8% and 10% of the severity of depression symptoms, respectively. However, over the past two weeks, life stressors had the highest predictability for depression (35.9%). The final model predicted a total of 51.9% variance for depression symptoms. In the final model, romantic relationship problems (ß = 2.30, S.E = 0.58; p<0.01) and academic performance problems (ß = 1.76, S.E = 0.60; p<0.01) over the past two weeks; and increased internet addiction severity (ß = 0.05, S.E = 0.02; p<0.01) was associated with significantly increased depression symptom severity, whereas Twitter use was associated with reduced depression symptom severity (ß = 1.88, S.E = 0.57; p<0.05). CONCLUSION: Despite life stressors being the largest predictor of depression symptom score severity, problematic online use also contributed significantly. Therefore, it is recommended that medical students' mental health care services consider digital wellbeing and its relationship with problematic online use as part of a more holistic depression prevention and resilience program.

Characterizing Responses to COVID-19 Vaccine Promotion on TikTok.

PURPOSE: The Alabama Department of Public Health (ADPH) sponsored a TikTok contest to improve vaccination rates among young people. This analysis sought to advance understanding of COVID-19 vaccine perceptions among ADPH contestants and TikTok commenters. APPROACH: This exploratory content analysis characterized sentiment and imagery in the TikTok videos and comments. Videos were coded by two reviewers and engagement metrics were collected for each video. SETTING: Publicly available TikTok videos entered into ADPH's contest with the hashtags #getvaccinatedAL and #ADPH between July 16 - August 6, 2021. PARTICIPANTS: ADPH contestants (n = 44) and TikTok comments (n = 502). METHOD: A content analysis was conducted; videos were coded by two reviewers and engagement metrics was collected for each video (e.g., reason for vaccination, content, type of vaccination received). Video comments were analyzed using VADER, a lexicon and rule-based sentiment analysis tool). RESULTS: Of 44 videos tagged with #getvaccinatedAL and #ADPH, 37 were related to the contest. Of the 37 videos, most cited family/friends and civic duty as their reason to get the COVID-19 vaccine. Videos were shared an average of 9 times and viewed 977 times. 70% of videos had comments, ranging from 0-61 (mean 44). Words used most in positively coded comments included, "beautiful," "smiling face emoji with 3 hearts," "masks," and "good.;" whereas words used most in negatively coded comments included "baby," "me," "chips," and "cold." CONCLUSION: Understanding COVID-19 vaccine sentiment expressed on social media platforms like TikTok can be a powerful tool and resource for public health messaging.

Monitoring real-time junior doctor sentiment from comments on a public social media platform: a retrospective observational study.

OBJECTIVES: To investigate whether sentiment analysis and topic modelling can be used to monitor the sentiment and opinions of junior doctors. DESIGN: Retrospective observational study based on comments on a social media website. SETTING: Every publicly available comment in r/JuniorDoctorsUK on Reddit from 1 January 2018 to 31 December 2021. PARTICIPANTS: 7707 Reddit users who commented in the r/JuniorDoctorsUK subreddit. MAIN OUTCOME MEASURE: Sentiment (scored -1 to +1) of comments compared with results of surveys conducted by the General Medical Council. RESULTS: Average comment sentiment was positive but varied significantly during the study period. Fourteen topics of discussion were identified, each associated with a different pattern of sentiment. The topic with the highest proportion of negative comments was the role of a doctor (38%), and the topic with the most positive sentiment was hospital reviews (72%). CONCLUSION: Some topics discussed in social media are comparable to those queried in traditional questionnaires, whereas other topics are distinctive and offer insight into what themes junior doctors care about. Events during the coronavirus pandemic may explain the sentiment trends in the junior doctor community. Natural language processing shows significant potential in generating insights into junior doctors' opinions and sentiment.

Rise of public e-learning opportunities in the context of COVID-19 pandemic-induced curtailment of face-to-face courses, exemplified by epidural catheterization on YouTube.

BACKGROUND: In the context of the coronavirus pandemic, countless face-to-face events as well as medical trainings were cancelled or moved to online courses, which resulted in increased digitalization in many areas. In the context of medical education, videos provide tremendous benefit for visualizing skills before they are practised. METHODS: Based on a previous investigation of video material addressing epidural catheterization available on the YouTube platform, we aimed to investigate new content produced in the context of the pandemic. Thus, a video search was conducted in May 2022. RESULTS: We identified twelve new videos since the pandemic with a significant improvement in the new content in terms of procedural items (p = 0.03) compared to the prepandemic video content. Video content released in the course of the COVID-19 pandemic was more often created by private content creators and were significantly shorter in total runtime than those from university and medical societies (p = 0.04). CONCLUSION: The profound changes in the learning and teaching of health care education in relation to the pandemic are largely unclear. We reveal improved procedural quality of predominantly privately uploaded content despite a shortened runtime compared to the prepandemic period. This might indicate that technical and financial hurdles to producing instructional videos by discipline experts have decreased. In addition to the teaching difficulties caused by the pandemic, this change is likely to be due to validated manuals on how to create such content. The awareness that medical education needs to be improved has grown, so platforms offer specialized sublevels for high-quality medical videos.

To troll or not to troll: Young adults' anti-social behaviour on social media.

BACKGROUND: Online anti-social behaviour is on the rise, reducing the perceived benefits of social media in society and causing a number of negative outcomes. This research focuses on the factors associated with young adults being perpetrators of anti-social behaviour when using social media. METHOD: Based on an online survey of university students in Canada (n = 359), we used PLS-SEM to create a model and test the associations between four factors (online disinhibition, motivations for cyber-aggression, self-esteem, and empathy) and the likelihood of being a perpetrator of online anti-social behaviour. RESULTS: The model shows positive associations between two appetitive motives for cyber-aggression (namely recreation and reward) and being a perpetrator. This finding indicates that young adults engage in online anti-social behaviour for fun and social approval. The model also shows a negative association between cognitive empathy and being a perpetrator, which indicates that perpetrators may be engaging in online anti-social behaviour because they do not understand how their targets feel.

Fake News and vaccine hesitancy in the COVID-19 pandemic in Brazil. / Fake news e hesitação vacinal no contexto da pandemia da COVID-19 no Brasil.

This paper presents the evolution of fake news disseminated about vaccines and the SARS-CoV-2 virus and its adverse impacts on the current Brazilian health crisis. This quantitative, empirical study is based on the notifications received by the Eu Fiscalizo app, through which the Instagram, Facebook, Twitter, and WhatsApp platforms were identified as the principal means for disseminating and sharing rumors and misinformation about COVID-19. We observed large-scale circulation of fake news about vaccines directly related to the Brazilian political polarization, which became prevalent four months after the first COVID-19 case was recorded in the country. We can conclude that this phenomenon was crucial in discouraging the adherence of segments of the Brazilian population to social distancing and vaccination campaigns.

Este artigo apresenta a evolução das notícias falsas disseminadas a respeito das vacinas e do vírus Sars-CoV-2 e os impactos negativos desse fenômeno sobre a crise sanitária que o Brasil atravessa. Trata-se de um estudo empírico quantitativo, realizado a partir das notificações recebidas pelo aplicativo Eu Fiscalizo, por meio do qual foi identificado o predomínio das plataformas Instagram, Facebook, Twitter e WhatsApp como os principais meios de difusão e compartilhamento de boatos e desinformações acerca da COVID-19. Foi observada a circulação em escala de fake news sobre vacinas, diretamente relacionadas à polarização política brasileira, tornando-se prevalente quatro meses depois de ser registrado o primeiro caso de COVID-19 no Brasil. Conclui-se que o fenômeno colaborou para desestimular a adesão de parcelas da população brasileira às campanhas de isolamento social e de vacinação.

Tracking the COVID-19 outbreak in India through Twitter: Opportunities for social media based global pandemic surveillance.

We investigated the utility of Twitter for conducting multi-faceted geolocation-centric pandemic surveillance, using India as an example. We collected over 4 million COVID19-related tweets related to the Indian outbreak between January and July 2021. We geolocated the tweets, applied natural language processing to characterize the tweets (eg., identifying symptoms and emotions), and compared tweet volumes with the numbers of confirmed COVID-19 cases. Tweet numbers closely mirrored the outbreak, with the 7-day average strongly correlated with confirmed COVID-19 cases nationally (Spearman r=0.944; p=0.001), and also at the state level (Spearman r=0.84, p=0.0003). Fatigue, Dyspnea and Cough were the top symptoms detected, while there was a significant increase in the proportion of tweets expressing negative emotions (eg., fear and sadness). The surge in COVID-19 tweets was followed by increased number of posts expressing concern about black fungus and oxygen supply. Our study illustrates the potential of social media for multi-faceted pandemic surveillance.

Physician and Biomedical Scientist Harassment on Social Media During the COVID-19 Pandemic.

This survey study assesses the frequency and nature of harassment on social media experienced by physicians, biomedical scientists, and trainees during the COVID-19 pandemic.

COVID-19 Vaccine Hesitancy: A Global Public Health and Risk Modelling Framework Using an Environmental Deep Neural Network, Sentiment Classification with Text Mining and Emotional Reactions from COVID-19 Vaccination Tweets.

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.

Tweeting for Health Using Real-time Mining and Artificial Intelligence-Based Analytics: Design and Development of a Big Data Ecosystem for Detecting and Analyzing Misinformation on Twitter.

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.

The impact of pandemic mental cognition on cultural values: an empirical study based on social media.

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.

Telemedicine and YouTube™: Video quality analysis before and after COVID-19 pandemic.

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.