The COVID-19 social media infodemic

Pseudoscience and fraudulent products for COVID-19 management

As of now, the COVID-19 pandemic has become uncontrolled and is spreading widely throughout the world. Additionally, new variants of the mutated viral variants have been found in some countries that are more dangerous than the original strain. Even vaccines cannot produce complete protective immunity against the newer strains of SARS-CoV-2. Due to such a dreadful situation, lots of fear and depression have been created among the public. People are looking for the treatment of the disease at any cost and there is a race in the market to provide treatment and make money, whether it is effective or not! In such a condition, many fraud products, remedies, and myths have come into the market, which is falsely claimed to be effective for the disease and can harm the patients. Hence, FDA has banned such products and remedies. In this review, we have compiled all such fraudulent and pseudosciences identified for COVID-19. Currently, in the pandemic time, health agencies are approving the repurposed medicines based on the small-scale clinical data for emergency uses that become ineffective (most of the cases) after large randomized clinical studies. Proper vigilance strategies need to be defined by the regulatory agencies of the nation and routine awareness programs shall be arranged for educating the people and healthcare workers on routine updates.

The COVID-19 scamdemic: A survey of phishing attacks and their countermeasures during COVID-19

The COVID-19 pandemic coincided with an equally-threatening scamdemic: a global epidemic of scams and frauds. The unprecedented cybersecurity concerns emerged during the pandemic sparked a torrent of research to investigate cyber-attacks and to propose solutions and countermeasures. Within the scamdemic, phishing was by far the most frequent type of attack. This survey paper reviews, summarises, compares and critically discusses 54 scientific studies and many reports by governmental bodies, security firms and the grey literature that investigated phishing attacks during COVID-19, or that proposed countermeasures against them. Our analysis identifies the main characteristics of the attacks and the main scientific trends for defending against them, thus highlighting current scientific challenges and promising avenues for future research and experimentation.

Media Bias and Factors Affecting the Impartiality of News Agencies during COVID-19

When COVID-19 was raging around the world, people were more fearful and anxious. In this context, the media should uphold impartiality and shoulder the responsibility of eliminating misinformation. Therefore, our research adopted sentiment analysis technologies to analyze the impartiality of news agencies and analyzed the factors that affect the impartiality of COVID-19-related articles about various countries. The SentiWordNet3.0 and bidirectional encoder representations from transformers (BERT) models were employed to analyze the articles and visualize the data. The following conclusions were redrawn in our research. During the pandemic, articles of some news agencies were not objective; the impartiality of news agencies was related to the reliability of news agencies instead of the bias of news agencies; there were obvious differences in the coverage and positivity of international news agencies to report the performance of COVID-19 prevention and control in different countries.

Assessing the Role of Social Bots During the COVID-19 Pandemic: Infodemic, Disagreement, and Criticism

BACKGROUND

Social media has changed the way we live and communicate, as well as offering unprecedented opportunities to improve many aspects of our lives, including health promotion and disease prevention. However, there is also a darker side to social media that is not always as evident as its possible benefits. In fact, social media has also opened the door to new social and health risks that are linked to health misinformation.

OBJECTIVE

This study aimed to study the role of social media bots during the COVID-19 outbreak.

METHODS

The Twitter streaming API was used to collect tweets regarding COVID-19 during the early stages of the outbreak. The Botometer tool was then used to obtain the likelihood of whether each account is a bot or not. Bot classification and topic-modeling techniques were used to interpret the Twitter conversation. Finally, the sentiment associated with the tweets was compared depending on the source of the tweet.

RESULTS

Regarding the conversation topics, there were notable differences between the different accounts. The content of nonbot accounts was associated with the evolution of the pandemic, support, and advice. On the other hand, in the case of self-declared bots, the content consisted mainly of news, such as the existence of diagnostic tests, the evolution of the pandemic, and scientific findings. Finally, in the case of bots, the content was mostly political. Above all, there was a general overriding tone of criticism and disagreement. In relation to the sentiment analysis, the main differences were associated with the tone of the conversation. In the case of self-declared bots, this tended to be neutral, whereas the conversation of normal users scored positively. In contrast, bots tended to score negatively.

CONCLUSIONS

By classifying the accounts according to their likelihood of being bots and performing topic modeling, we were able to segment the Twitter conversation regarding COVID-19. Bot accounts tended to criticize the measures imposed to curb the pandemic, express disagreement with politicians, or question the veracity of the information shared on social media.

Emotional profiling and cognitive networks unravel how mainstream and alternative press framed AstraZeneca, Pfizer and COVID-19 vaccination campaigns

COVID-19 vaccines have been largely debated by the press. To understand how mainstream and alternative media debated vaccines, we introduce a paradigm reconstructing time-evolving narrative frames via cognitive networks and natural language processing. We study Italian news articles massively re-shared on Facebook/Twitter (up to 5 million times), covering 5745 vaccine-related news from 17 news outlets over 8 months. We find consistently high trust/anticipation and low disgust in the way mainstream sources framed “vaccine/vaccino”. These emotions were crucially missing in alternative outlets. News titles from alternative sources framed “AstraZeneca” with sadness, absent in mainstream titles. Initially, mainstream news linked mostly “Pfizer” with side effects (e.g. “allergy”, “reaction”, “fever”). With the temporary suspension of “AstraZeneca”, negative associations shifted: Mainstream titles prominently linked “AstraZeneca” with side effects, while “Pfizer” underwent a positive valence shift, linked to its higher efficacy. Simultaneously, thrombosis and fearful conceptual associations entered the frame of vaccines, while death changed context, i.e. rather than hopefully preventing deaths, vaccines could be reported as potential causes of death, increasing fear. Our findings expose crucial aspects of the emotional narratives around COVID-19 vaccines adopted by the press, highlighting the need to understand how alternative and mainstream media report vaccination news.

Analyzing preventive precautions to limit spread of COVID-19

With the global spread of COVID-19, the governments advised the public for adopting safety precautions to limit its spread. The virus spreads from people, contaminated places, and nozzle droplets that necessitate strict precautionary measures. Consequently, different safety precautions have been implemented to fight COVID-19 such as wearing a facemask, restriction of social gatherings, keeping 6 feet distance, etc. Despite the warnings, highlighted need for such measures, and the increasing severity of the pandemic situation, the expected number of people adopting these precautions is low. This study aims at assessing and understanding the public perception of COVID-19 safety precautions, especially the use of facemask. A unified framework of sentiment lexicon with the proposed ensemble EB-DT is devised to analyze sentiments regarding safety precautions. Extensive experiments are performed with a large dataset collected from Twitter. In addition, the factors leading to a negative perception of safety precautions are analyzed by performing topic analysis using the Latent Dirichlet allocation algorithm. The experimental results reveal that 12% of the tweets correspond to negative sentiments towards facemask precaution mainly by its discomfort. Analysis of change in peoples’ sentiment over time indicates a gradual increase in the positive sentiments regarding COVID-19 restrictions.

Digital artifacts reveal development and diffusion of climate research

Research for development organizations generate tremendous amount of accessible knowledge, but given their scale, time and resource constraints, the impact of outputs is not systematically analyzed. This is because traditional bibliometric analyses present limitations to synthesize accumulated knowledge and retrofitting indicators to historical outputs. To address these shortcomings, this study proposes an integrated, web-based approach to systematically analyze the production and diffusion of knowledge from large-scale research programs, using climate research of the International Maize and Wheat Improvement Center (CIMMYT) as a case study. Our analytical framework employs text mining, social network analysis and hyperlink analysis to an unstructured mass of publicly available digital artifacts such as institutional repositories, citation databases, and social media to uncover narratives, dynamics, and relationships. Findings show CIMMYT’s climate research is strongly incorporated into a holistic systems approach and that the institution is actively engaged in knowledge exchanges with key actors from the scientific, development and public policy communities. The proposed analytical framework establishes an effective approach for research for development organizations to leverage existing online data sources to assess the extent of their knowledge production, dissemination, and reach.

Seeing Should Probably not be Believing: The Role of Deceptive Support in COVID-19 Misinformation on Twitter

With the spread of the SARS-CoV-2, enormous amounts of information about the pandemic are disseminated through social media platforms such as Twitter. Social media posts often leverage the trust readers have in prestigious news agencies and cite news articles as a way of gaining credibility. Nevertheless, it is not always the case that the cited article supports the claim made in the social media post. We present a cross-genre ad hoc pipeline to identify whether the information in a Twitter post (i.e., a “Tweet”) is indeed supported by the cited news article. Our approach is empirically based on a corpus of over 46.86 million Tweets and is divided into two tasks: (i) development of models to detect Tweets containing claim and worth to be fact-checked and (ii) verifying whether the claims made in a Tweet are supported by the newswire article it cites. Unlike previous studies that detect unsubstantiated information by post hoc analysis of the patterns of propagation, we seek to identify reliable support (or the lack of it) before the misinformation begins to spread. We discover that nearly half of the Tweets (43.4%) are not factual and hence not worth checking – a significant filter, given the sheer volume of social media posts on a platform such as Twitter. Moreover, we find that among the Tweets that contain a seemingly factual claim while citing a news article as supporting evidence, at least 1% are not actually supported by the cited news, and are hence misleading.

Finding answers to COVID-19-specific questions: An information retrieval system based on latent keywords and adapted TF-IDF

The scientific community has reacted to the COVID-19 outbreak by producing a high number of literary works that are helping us to understand a variety of topics related to the pandemic from different perspectives. Dealing with this large amount of information can be challenging, especially when researchers need to find answers to complex questions about specific topics. We present an Information Retrieval System that uses latent information to select relevant works related to specific concepts. By applying Latent Dirichlet Allocation (LDA) models to documents, we can identify key concepts related to a specific query and a corpus. Our method is iterative in that, from an initial input query defined by the user, the original query is expanded for each subsequent iteration. In addition, our method is able to work with a limited amount of information per article. We have tested the performance of our proposal using human validation and two evaluation strategies, achieving good results in both of them. Concerning the first strategy, we performed two surveys to determine the performance of our model. For all the categories that were studied, precision was always greater than 0.6, while accuracy was always greater than 0.8. The second strategy also showed good results, achieving a precision of 1.0 for one category and scoring over 0.7 points overall.

Physical Distancing and Social Media Use in Emerging Adults and Adults During the COVID-19 Pandemic: Large-scale Cross-sectional and Longitudinal Survey Study

Background

Although emerging adults play a role in the spread of COVID-19, they are less likely to develop severe symptoms after infection. Emerging adults’ relatively high use of social media as a source of information raises concerns regarding COVID-19–related behavioral compliance (ie, physical distancing) in this age group.

Objective

This study aimed to investigate physical distancing among emerging adults in comparison with adults and examine the role of using social media for COVID-19 news and information in this regard. In addition, this study explored the relationship between physical distancing and using different social media platforms and sources.

Methods

The secondary data of a large-scale longitudinal national survey (N=123,848) between April and November 2020 were used. Participants indicated, ranging from 1 to 8 waves, how often they were successful in keeping a 1.5-m distance on a 7-point Likert scale. Participants aged between 18 and 24 years were considered emerging adults, and those aged >24 years were considered adults. In addition, a dummy variable was created to indicate per wave whether participants used social media for COVID-19 news and information. A subset of participants received follow-up questions to determine which platforms they used and what sources of news and information they had seen on social media. All preregistered hypotheses were tested with linear mixed-effects models and random intercept cross-lagged panel models.

Results

Emerging adults reported fewer physical distancing behaviors than adults (β=−.08, t_86,213.83=−26.79; P<.001). Moreover, emerging adults were more likely to use social media for COVID-19 news and information (b=2.48; odds ratio 11.93 [95% CI=9.72-14.65]; SE 0.11; Wald=23.66; P<.001), which mediated the association with physical distancing but only to a small extent (indirect effect: b=−0.03, 95% CI −0.04 to −0.02). Contrary to our hypothesis, the longitudinal random intercept cross-lagged panel model showed no evidence that physical distancing was not influenced by social media use in the previous wave. However, evidence indicated that social media use affects subsequent physical distancing behavior. Moreover, additional analyses showed that the use of most social media platforms (ie, YouTube, Facebook, and Instagram) and interpersonal communication were negatively associated with physical distancing, whereas other platforms (ie, LinkedIn and Twitter) and government messages had no or small positive associations with physical distancing.

Conclusions

In conclusion, we should be vigilant with regard to the physical distancing of emerging adults, but the study results did not indicate concerns regarding the role of social media for COVID-19 news and information. However, as the use of some social media platforms and sources showed negative associations with physical distancing, future studies should more carefully examine these factors to better understand the associations between social media use for news and information and behavioral interventions in times of crisis.

The Association between Types of COVID-19 Information Source and the Avoidance of Child Health Checkups in Japan: Findings from the JACSIS 2021 Study

The coronavirus disease 2019 (COVID-19) pandemic can affect children's well-being through mothers' avoidance of health checkups for children due to media portrayal of the disease. This study investigated the association between the type of information source for COVID-19 received by mothers and the avoidance of their children's health checkups. The study was an online-based survey, and the participants comprised 5667 postpartum women with children aged under 2 years during the study period. We analyzed the analytic sample and three groups of women with children aged 0-3 months, 4-6 months, and 6 months or older according to the timing of children's health checkups in Japan. Among the participants, 382 women (6.7%) avoided their children's health checkups. Multivariate logistic regression analysis revealed that mothers with children over 6 months who used magazines as an information source about COVID-19 tended to avoid their children's health checkups (adjusted odds ratio (aOR): 3.19; 95% confidence interval (CI): 1.68-6.05) compared with those who did not. In contrast, those using public websites were less likely to avoid their children's health checkups (aOR 0.58, 95% CI 0.43-0.77). This study showed that specific types of information source on COVID-19 could have varying effects on mothers' decisions about their children's health checkups.

Editorial: The Sociology of Vaccines

In this editorial, we identify the key questions requiring further exploration in the sociology of vaccines. In doing so, we discuss the socio-structural forces shaping views towards knowledge about and access to vaccination, trust in vaccines and regulators/decision makers, the associated problem of financial interests in vaccine development and regulation, and global vaccine inequalities. Across the breadth of these issues, we additionally identify a range of theoretical perspectives and conceptual directions that sociologists might utilise when producing innovative empirical, methodological and theoretical research on vaccination relating to risk and uncertainty, conflicts of interest, power and inequality.

Visualizing Social Media Research in the Age of COVID-19

During the last three years, numerous research papers have been reported which use social media data to explore several issues related to the COVID-19 pandemic. Bibliometric methods in this work are used to analyze 1427 peer-reviewed documents from the last three years extracted from the Web of Science database. The results of this study show that there was high growth in publications in open access journals with an annual rate reaching 19.3% and they also identify the top cited journals and research papers. The thematic analysis of papers shows that research topics related to social media for surveillance and monitoring of public attitudes and perceptions, mental health, misinformation, and fake news are important and well-developed, whereas topics related to distance-learning education with social media are emerging. The results also show that the USA, China, and the UK have published many papers and received a high number of citations because of their strong international collaboration.

Distinct spreading patterns induced by coexisting channels in information spreading dynamics

In modern society, new communication channels and social platforms remarkably change the way of people receiving and sharing information, but the influences of these channels on information spreading dynamics have not been fully explored, especially in the aspects of outbreak patterns. To this end, based on a susceptible-accepted-recovered model, we examined the outbreak patterns of information spreading in a two-layered network with two coexisting channels: the intra-links within a layer and the inter-links across layers. Depending on the inter-layer coupling strength, i.e., average node degree and transmission probability between the two layers, we observed three different spreading patterns: (i) a localized outbreak with weak inter-layer coupling, (ii) two peaks with a time-delay outbreak appear for an intermediate coupling, and (iii) a synchronized outbreak for a strong coupling. Moreover, we showed that even though the average degree between the two layers is small, a large transmission probability still can compensate and promote the information spread from one layer to another, indicating by that the critical average degree decreases as a power law with transmission probability between the two layers. Additionally, we found that a large gap closed to the critical inter-layer average degree appears in the phase space of theoretical analysis, which indicates the emergence of a global large-scope outbreak. Our findings may, therefore, be of significance for understanding the outbreak behaviors of information spreading in real world.

Psychological inoculation can reduce susceptibility to misinformation in large rational agent networks

The unchecked spread of misinformation is recognized as an increasing threat to public, scientific and democratic health. Online networks are a contributing cause of this spread, with echo chambers and polarization indicative of the interplay between the search behaviours of users and reinforcement processes within the system they inhabit. Recent empirical work has focused on interventions aimed at inoculating people against misinformation, yielding success on the individual level. However, given the evolving, dynamic information context of online networks, important questions remain regarding how such inoculation interventions interact with network systems. Here we use an agent-based model of a social network populated with belief-updating users. We find that although equally rational agents may be assisted by inoculation interventions to reject misinformation, even among such agents, intervention efficacy is temporally sensitive. We find that as beliefs disseminate, users form self-reinforcing echo chambers, leading to belief consolidation-irrespective of their veracity. Interrupting this process requires 'front-loading' of inoculation interventions by targeting critical thresholds of network users before consolidation occurs. We further demonstrate the value of harnessing tipping point dynamics for herd immunity effects, and note that inoculation processes do not necessarily lead to increased rates of 'false-positive' rejections of truthful communications.

The use of the Dark Web as a COVID-19 information source: A three-country study

The Dark Web (i.e., the anonymous web or Darknet) contains potentially harmful COVID-19-related information and content such as conspiracy theories and forged certificates. The Dark Web may particularly attract individuals who are suspicious about the pandemic, but there is no research concerning the use of the Dark Web as a COVID-19 information source. In this study, we investigated the role of COVID-19 skepticism, online activities, and loneliness in the use of the Dark Web platforms as a COVID-19 information source. The data (N = 3000) were gathered in April 2021 from 18 to 75-year-old respondents from Finland (n = 1000), Sweden (n = 1000) and the United Kingdom (n = 1000). The respondents were asked how often they had utilized Dark Web platforms (for example via TOR-network) as a COVID-19 information source during the pandemic. Self-reported measures of institutional trust, anti-vaccine stances, restriction obedience, online activities, and loneliness were used as predictors in the logistic regression model. Age, gender, and education were also included in the model. The Dark Web use was more prevalent in the UK and Sweden. There was an association between anti-vaccine stances and active Dark Web use in the UK and Sweden, while low institutional trust predicted use among Finnish respondents. In all countries, restriction disobedience was related to Dark Web use as a COVID-19 information source. Frequent online gambling, increased social media use, and loneliness predicted Dark Web use, and these associations were even stronger among frequent Dark Web users than occasional users. Younger age and male gender were also associated with Dark Web use. The unregulated nature of the Dark Web makes it a risky alternative to COVID-19 information, attracting individuals who are suspicious about the pandemic and overall active online users. Misleading information and availability of forged certificates on the Dark Web challenge official health policies, posing significant risks for both individual and public health.

Using WhatsApp During the COVID-19 Pandemic and the Emotions and Perceptions of Users

Background

Purpose

Methods and Participants

Results

Conclusion

COVID information and masking behaviors in U.S. adolescents: Findings from the Adolescent Brain Cognitive Development (ABCD) Study

Adolescents are particularly vulnerable to health misinformation and are at risk for suboptimal adherence to protective health behaviors in the COVID-19 pandemic. Guided by factors consistent with the theories of planned behavior and rumor transmission, this study sought to analyze the impact of multiple information sources, including social media, television media, internet and parental counseling, on masking behaviors in adolescents. Responses from the December 2020 COVID-19 survey, representing 4,106 U.S. adolescents ages 12-14 from the Adolescent Brain Cognitive Development Study (ABCD) were analyzed. The majority of parents (61.1%) reported counseling their children on the importance of wearing masks all the time in the past week. A minority of adolescents reported more than one hour of daily exposure to COVID-19 related information on social media (9.1%), the internet (4.3%) and television (10.2%). In unadjusted and adjusted models, greater frequency of parental counseling and exposure to COVID-19 television or social media were associated with 'always masking' behaviors. Our findings provide support for the importance of parent counseling and suggest that socialmedia and television may overall support rather than dissuade protective COVID-19 health behaviors in adolescents.

Co-design of a Teaching-Learning Sequence to Address COVID‑19 as a Socio‑scientific Issue in an Infodemic Context

In the post-truth era, one challenge facing science education is the circulation of fake news that distorts the information available for decision-making on issues that have a scientific basis and are controversial for society. In this work, we aimed at designing a learning environment with the objective of equipping students with skills that allow them to deal with socio-scientific issues (SSI) in an infodemic context. To this end, we proposed an educational innovation through design-based research, which was oriented to the treatment of information disseminated in the media and social networks related to COVID-19. We divided this information into four major constructs: virus and disease dynamics; pandemic and environmental crisis; hygiene and protocols; and vaccines, potential solutions, and pharmaceutical industry. On the basis of the activities of the didactic sequence, which included class discussion, interviews with the immediate environment, audiovisual productions, and a final plenary, we identified criteria that students applied to trust or not trust the circulating information and a series of strategies to corroborate the information. In addition, framing COVID-19 as an SSI allowed the discussion of curricular content in science and on sociocultural dimensions that cross the pandemic. Based on the implementation of the teaching-learning sequence, we conclude that the proposed activities favored reflection on critical thinking and awareness of the responsibilities they have as potential disseminators and/or generators of information.

Investigation of COVID-19 Misinformation in Arabic on Twitter: Content Analysis

Background

The COVID-19 pandemic has been occurring concurrently with an infodemic of misinformation about the virus. Spreading primarily on social media, there has been a significant academic effort to understand the English side of this infodemic. However, much less attention has been paid to the Arabic side.

Objective

There is an urgent need to examine the scale of Arabic COVID-19 disinformation. This study empirically examines how Arabic speakers use specific hashtags on Twitter to express antivaccine and antipandemic views to uncover trends in their social media usage. By exploring this topic, we aim to fill a gap in the literature that can help understand conspiracies in Arabic around COVID-19.

Methods

This study used content analysis to understand how 13 popular Arabic hashtags were used in antivaccine communities. We used Twitter Academic API v2 to search for the hashtags from the beginning of August 1, 2006, until October 10, 2021. After downloading a large data set from Twitter, we identified major categories or topics in the sample data set using emergent coding. Emergent coding was chosen because of its ability to inductively identify the themes that repeatedly emerged from the data set. Then, after revising the coding scheme, we coded the rest of the tweets and examined the results. In the second attempt and with a modified codebook, an acceptable intercoder agreement was reached (Krippendorff α≥.774).

Results

In total, we found 476,048 tweets, mostly posted in 2021. First, the topic of infringing on civil liberties (n=483, 41.1%) covers ways that governments have allegedly infringed on civil liberties during the pandemic and unfair restrictions that have been imposed on unvaccinated individuals. Users here focus on topics concerning their civil liberties and freedoms, claiming that governments violated such rights following the pandemic. Notably, users denounce government efforts to force them to take any of the COVID-19 vaccines for different reasons. This was followed by vaccine-related conspiracies (n=476, 40.5%), including a Deep State dictating pandemic policies, mistrusting vaccine efficacy, and discussing unproven treatments. Although users tweeted about a range of different conspiracy theories, mistrusting the vaccine’s efficacy, false or exaggerated claims about vaccine risks and vaccine-related diseases, and governments and pharmaceutical companies profiting from vaccines and intentionally risking the general public health appeared the most. Finally, calls for action (n=149, 12.6%) encourage individuals to participate in civil demonstrations. These calls range from protesting to encouraging other users to take action about the vaccine mandate. For each of these categories, we also attempted to trace the logic behind the different categories by exploring different types of conspiracy theories for each category.

Conclusions

Based on our findings, we were able to identify 3 prominent topics that were prevalent amongst Arabic speakers on Twitter. These categories focused on violations of civil liberties by governments, conspiracy theories about the vaccines, and calls for action. Our findings also highlight the need for more research to better understand the impact of COVID-19 disinformation on the Arab world.

The Influence of Vicarious Fear-Learning in “Infecting” Reactive Action Inhibition

Since the dawn of cognitive neuroscience, emotions have been recognized to impact on several executive processes, such as action inhibition. However, the complex interplay between emotional stimuli and action control is not yet fully understood. One way to measure inhibitory control is the stop-signal task (SST), which estimates the ability to cancel outright an action to the presentation of a stop signal by means of the stop-signal reaction times (SSRTs). Impaired as well as facilitated action control has been found when faced with intrinsic emotional stimuli as stop signals in SSTs. Here, we aimed at investigating more deeply the power of negative stimuli to influence our action control, testing the hypothesis that a previously neutral stimulus [i.e., the image of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)], which has been conditioned through vicarious fear learning, has the same impact on reactive action inhibition performance as an intrinsically negative stimulus (i.e., a fearful face or body). Action control capabilities were tested in 90 participants by means of a SST, in which the stop signals were represented by different negative stimuli. Results showed that the SARS-CoV-2 image enhanced the ability to suppress an ongoing action similarly to observing fearful facial expressions or fearful body postures. Interestingly, we found that this effect was predicted by impulsivity traits: for example, the less self-control the participants had, the less they showed emotional facilitation for inhibitory performance. These results demonstrated that vicarious fear learning has a critical impact on cognitive abilities, making a neutral image as threatening as phylogenetically innate negative stimuli and able to impact on our behavioral control.

FOMO (fate of online media only) in infectious disease modeling: a review of compartmental models

Mathematical models played in a major role in guiding policy decisions during the COVID-19 pandemic. These models while focusing on the spread and containment of the disease, largely ignored the impact of media on the disease transmission. Media plays a major role in shaping opinions, attitudes and perspectives and as the number of people online increases, online media are fast becoming a major source for news and health related information and advice. Consequently, they may influence behavior and in due course disease dynamics. Unlike traditional media, online media are themselves driven and influenced by their users and thus have unique features. The main techniques used to incorporate online media mathematically into compartmental models, with particular reference to the ongoing COVID-19 pandemic are reviewed. In doing so, features specific to online media that have yet to be fully integrated into compartmental models such as misinformation, different time scales with regards to disease transmission and information, time delays, information super spreaders, the predatory nature of online media and other factors are identified together with recommendations for their incorporation.

Comparing Worldwide, National, and Independent Notifications about Adverse Drug Reactions Due to COVID-19 Vaccines

The rapid development of effective vaccines against COVID-19 is an extraordinary achievement. However, no medical product can ever be considered risk-free. Several countries have a pharmacovigilance system that detects, assesses, understands, and prevents possible adverse effects of a drug. To benefit from such huge data sources, specialists and researchers need advanced big data analysis tools able to extract value and find valuable insights. This paper defines a general framework for a pharmaceutical data analysis application that provides a predefined (but extensible) set of functions for each data processing step (i.e., data collection, filtering, enriching, analysis, and visualization). As a case study, we present here an analysis of the potential side effects observed following the administration of the COVID-19 vaccines. The experimental evaluation shows that: (i) most adverse events can be classified as non-serious and concern muscle/joint pain, chills and nausea, headache, and fatigue; (ii) the notification rate is higher in the age group 20–39 years and decreases in older age groups and in very young people.

Facilitators and barriers to COVID-19 vaccination uptake among ethnic minorities: A qualitative study in primary care

Introduction

COVID-19 vaccination effectively reduces severe disease and death from COVID-19. However, both vaccine uptake and intention to vaccinate differ amongst population groups. Vaccine hesitancy is highest amongst specific ethnic minority groups. There is very limited understanding of the barriers and facilitators to COVID-19 vaccine uptake in Black and South Asian ethnicities. Therefore, we aimed to explore COVID-19 vaccination hesitancy in primary care patients from South Asian (Bangladeshi/Pakistani) and Black or Black British/African/Caribbean/Mixed ethnicities.

Methods

Patients from the above ethnicities were recruited using convenience sampling in four London general practices. Telephone interviews were conducted, using an interpreter if necessary, covering questions on the degree of vaccine hesitancy, barriers and potential facilitators, and decision-making. Interviews were transcribed verbatim and thematically analysed. Data collection and analysis occurred concurrently with the iterative development of the topic guide and coding framework. Key themes were conceptualised through discussion with the wider team.

Results

Of thirty-eight interviews, 55% (21) of these were in Black or Black British/African/Caribbean/Mixed ethnicities, 32% (12) in Asian / British Asian and 13% (5) in mixed Black and White ethnicities. Key themes included concerns about the speed of vaccine roll-out and potential impacts on health, mistrust of official information, and exposure to misinformation. In addition, exposure to negative messages linked to vaccination appears to outweigh positive messages received. Facilitators included the opportunity to discuss concerns with a healthcare professional, utilising social influences via communities and highlighting incentives.

Conclusion

COVID-19 has disproportionately impacted ethnic minority groups. Vaccination is an effective strategy for mitigating risk. We have demonstrated factors contributing to vaccine reluctance, hesitancy and refusal and highlighted levers for change.

[Communication in times of pandemic: information, disinformation, and provisional lessons from the coronavirus crisis]

Objetivo

Caracterizar la infodemia asociada a la pandemia de COVID-19 y analizar, en el caso de España, el consumo de información, la confianza en las fuentes, el papel de los medios y la comunicación gubernamental, así como discutir algunas lecciones de comunicación provisionales a partir de los estudios sobre esta y otras pandemias.

Método

Búsqueda bibliográfica en PubMed y Scopus, y revisión de los documentos seleccionados con los criterios de relevancia para los objetivos y el ámbito español.

Resultados

La pandemia de COVID-19 se ha solapado con una infodemia que ha provocado la mayor avalancha de desinformación conocida y dificultades para encontrar información fiable a casi la mitad de la población. En España, el consumo de información se ha concentrado en los medios tradicionales y WhatsApp. Los medios han ayudado a entender la pandemia y han sido relativamente bien valorados, aunque inspiran menos confianza que en otros países occidentales. El análisis de la comunicación del Gobierno español muestra algunos errores, como la difusión de mensajes demasiado tranquilizadores al inicio de la pandemia, la falta de transparencia, el exceso de información y el modelo de portavocía adoptado.

Conclusiones

El conocimiento de la infodemia asociada a la COVID-19 es fragmentario e insuficiente. Aunque la crisis sanitaria no está cerrada para ser debidamente evaluada, es posible extraer algunas lecciones comunicativas provisionales. La complejidad del fenómeno de la desinformación exige considerar la infodemiología como una disciplina científica para conocer la propagación de la desinformación igual que la de la enfermedad.

MetaDetector: Meta Event Knowledge Transfer for Fake News Detection

The blooming of fake news on social networks has devastating impacts on society, economy, and public security. Although numerous studies are conducted for the automatic detection of fake news, the majority tend to utilize deep neural networks to learn event-specific features for superior detection performance on specific datasets. However, the trained models heavily rely on the training datasets and are infeasible to apply to upcoming events due to the discrepancy between event distributions. Inspired by domain adaptation theories, we propose an end-to-end adversarial adaptation network, dubbed as MetaDetector , to transfer meta knowledge (event-shared features) between different events. Specifically, MetaDetector pushes the feature extractor and event discriminator to eliminate event-specific features and preserve required meta knowledge by adversarial training. Furthermore, the pseudo-event discriminator is utilized to evaluate the importance of news records in historical events to obtain partial knowledge that are discriminative for detecting fake news. Under the coordinated optimization among all the submodules, MetaDetector accurately transfers the meta knowledge of historical events to the upcoming event for fact checking. We conduct extensive experiments on two real-world datasets collected from Sina Weibo and Twitter. The experimental results demonstrate that MetaDetector outperforms the state-of-the-art methods, especially when the distribution discrepancy between events is significant.

Investigating the Obsessive and Compulsive Features of Cyberchondria: A Holistic Review

Background

Cyberchondria has been brought into sharp focus during the COVID-19 health emergency; it refers to individuals who obsessively and compulsively search for health information online, resulting in excessive health concerns. Recent scholarship focuses on its obsessive and compulsive aspect, following a biopsychosocial approach as opposed to a pathology of health anxiety. It lacks interpretation of the socio-psychological dynamics between the dimensions.

Objective

This review aims to propose a holistic view toward understanding cyberchondria as an obsessive–compulsive syndrome and considers possible interventions. It specifically seeks to explain cyberchondria from diversified mediator variables and to pinpoint connections between each perspective.

Methodology

Comprehensive searches of databases such as PubMed and Springer were conducted to identify English articles relating to cyberchondria from 2001 to 2022. Based on a systematic filtering process, 27 articles were finally reviewed.

Findings

The authors compare and confirm three forecasts to predict cyberchondria, associating it with individual metacognition, uncertainty of unverified information, and algorithm-driven, biased information environments.

Value

Theoretically, a holistic framework is proposed to explain the obsessive and compulsive features of cyberchondria. Clinically, the research calls for more professional psychoeducation and chain screening of cyberchondria and other psychological disorders. Socially, it promotes support for risk-sensitive, information-deficient groups during pandemics like COVID-19. It also stresses more careful use of algorithm-driven search engine technology for platforms delivering medical information. Future research may explore areas such as the association between cyberchondria and other social-related disorders, as well as correlations among cyberchondria, obsessive and compulsive disorders, medical trust, and algorithm-driven search results.

The Asymmetric Influence of Emotion in the Sharing of COVID-19 Science on Social Media: Observational Study

BACKGROUND

Unlike past pandemics, COVID-19 is different to the extent that there is an unprecedented surge in both peer-reviewed and preprint research publications, and important scientific conversations about it are rampant on online social networks, even among laypeople. Clearly, this new phenomenon of scientific discourse is not well understood in that we do not know the diffusion patterns of peer-reviewed publications vis-à-vis preprints and what makes them viral.

OBJECTIVE

This paper aimed to examine how the emotionality of messages about preprint and peer-reviewed publications shapes their diffusion through online social networks in order to inform health science communicators' and policy makers' decisions on how to promote reliable sharing of crucial pandemic science on social media.

METHODS

We collected a large sample of Twitter discussions of early (January to May 2020) COVID-19 medical research outputs, which were tracked by Altmetric, in both preprint servers and peer-reviewed journals, and conducted statistical analyses to examine emotional valence, specific emotions, and the role of scientists as content creators in influencing the retweet rate.

RESULTS

Our large-scale analyses (n=243,567) revealed that scientific publication tweets with positive emotions were transmitted faster than those with negative emotions, especially for messages about preprints. Our results also showed that scientists' participation in social media as content creators could accentuate the positive emotion effects on the sharing of peer-reviewed publications.

CONCLUSIONS

Clear communication of critical science is crucial in the nascent stage of a pandemic. By revealing the emotional dynamics in the social media sharing of COVID-19 scientific outputs, our study offers scientists and policy makers an avenue to shape the discussion and diffusion of emerging scientific publications through manipulation of the emotionality of tweets. Scientists could use emotional language to promote the diffusion of more reliable peer-reviewed articles, while avoiding using too much positive emotional language in social media messages about preprints if they think that it is too early to widely communicate the preprint (not peer reviewed) data to the public.

Charting cognition: Mapping public understanding of COVID-19

The COVID-19 pandemic of the last 2 years (and counting) disrupted commerce, travel, workplaces, habits, and-of course-health, the world over. This study aimed to capture snapshots of the perceptions and misperceptions of COVID-19 among 27 participants from three US municipalities. These perspectives are analyzed through thematic analyses and concept maps. Such snapshots, particularly as viewed through the lens of narrative sense-making theory, capture a sample of cognitions at this unique moment in history: a little over 1 year into the COVID-19 pandemic. Findings suggest that the (mis)perceptions captured are predominantly conveyed via narratives of participants' personal experiences, and that the themes of attitudes toward precautionary measures, uncertainty, and the muddied science communication environment are prevalent. These themes suggest several salient targets for future research and current science communication, such as a focus on basic explainers, vaccinations' safety and effectiveness and the necessity of uncertainty in the practice of science.

A Report on Statistics of an Online Self-screening Platform for COVID-19 and Its Effectiveness in Iran

BACKGROUND

The most recent emerging infectious disease, coronavirus disease 2019 (COVID-19), is pandemic now. Iran is a country with community transmission of the disease. Telehealth tools have been proved to be useful in controlling public health disasters. We developed an online self-screening platform to offer a population-wide strategy to control the massive influx to medical centers.

METHODS

We developed a platform operating based on given history by participants, including sex, age, weight, height, location, primary symptoms and signs, and high risk past medical histories. Based on a decision-making algorithm, participants were categorized into four levels of suspected cases, requiring diagnostic tests, supportive care, not suspected cases. We made comparisons with Iran STEPs (STEPwise approach to Surveillance) 2016 study and data from the Statistical Centre of Iran to assess population representativeness of data. Also, we made a comparison with officially confirmed cases to investigate the effectiveness of the platform. A multilevel mixed-effects Poisson regression was used to check the association of visiting platform and deaths caused by COVID-19.

RESULTS

About 310 000 individuals participated in the online self-screening platform in 33 days. The majority of participants were in younger age groups, and males involved more. A significant number of participants were screened not to be suspected or needing supportive care, and only 10.4% of males and 12.0% of females had suspected results of COVID-19. The penetration of the platform was assessed to be acceptable. A correlation coefficient of 0.51 was calculated between suspected results and confirmed cases of the disease, expressing the platform's effectiveness.

CONCLUSION

Implementation of a proper online self-screening tool can mitigate population panic during wide-spread epidemics and relieve massive influx to medical centers. Also, an evidence-based education platform can help fighting infodemic. Noticeable utilization and verified effectiveness of such platform validate the potency of telehealth tools in controlling epidemics and pandemics.

COVID-19 Misinformation Detection: Machine-Learned Solutions to the Infodemic

Background

The volume of COVID-19-related misinformation has long exceeded the resources available to fact checkers to effectively mitigate its ill effects. Automated and web-based approaches can provide effective deterrents to online misinformation. Machine learning-based methods have achieved robust performance on text classification tasks, including potentially low-quality-news credibility assessment. Despite the progress of initial, rapid interventions, the enormity of COVID-19-related misinformation continues to overwhelm fact checkers. Therefore, improvement in automated and machine-learned methods for an infodemic response is urgently needed.

Objective

The aim of this study was to achieve improvement in automated and machine-learned methods for an infodemic response.

Methods

We evaluated three strategies for training a machine-learning model to determine the highest model performance: (1) COVID-19-related fact-checked data only, (2) general fact-checked data only, and (3) combined COVID-19 and general fact-checked data. We created two COVID-19-related misinformation data sets from fact-checked "false" content combined with programmatically retrieved "true" content. The first set contained ~7000 entries from July to August 2020, and the second contained ~31,000 entries from January 2020 to June 2022. We crowdsourced 31,441 votes to human label the first data set.

Results

The models achieved an accuracy of 96.55% and 94.56% on the first and second external validation data set, respectively. Our best-performing model was developed using COVID-19-specific content. We were able to successfully develop combined models that outperformed human votes of misinformation. Specifically, when we blended our model predictions with human votes, the highest accuracy we achieved on the first external validation data set was 99.1%. When we considered outputs where the machine-learning model agreed with human votes, we achieved accuracies up to 98.59% on the first validation data set. This outperformed human votes alone with an accuracy of only 73%.

Conclusions

External validation accuracies of 96.55% and 94.56% are evidence that machine learning can produce superior results for the difficult task of classifying the veracity of COVID-19 content. Pretrained language models performed best when fine-tuned on a topic-specific data set, while other models achieved their best accuracy when fine-tuned on a combination of topic-specific and general-topic data sets. Crucially, our study found that blended models, trained/fine-tuned on general-topic content with crowdsourced data, improved our models' accuracies up to 99.7%. The successful use of crowdsourced data can increase the accuracy of models in situations when expert-labeled data are scarce. The 98.59% accuracy on a "high-confidence" subsection comprised of machine-learned and human labels suggests that crowdsourced votes can optimize machine-learned labels to improve accuracy above human-only levels. These results support the utility of supervised machine learning to deter and combat future health-related disinformation.

Using Twitter Data to Understand Public Perceptions of Approved versus Off-label Use for COVID-19-related Medications

Objective

Understanding public discourse on emergency use of unproven therapeutics is essential to monitor safe use and combat misinformation. We developed a natural language processing-based pipeline to understand public perceptions of and stances on coronavirus disease 2019 (COVID-19)-related drugs on Twitter across time.

Methods

This retrospective study included 609 189 US-based tweets between January 29, 2020 and November 30, 2021 on 4 drugs that gained wide public attention during the COVID-19 pandemic: (1) Hydroxychloroquine and Ivermectin, drug therapies with anecdotal evidence; and (2) Molnupiravir and Remdesivir, FDA-approved treatment options for eligible patients. Time-trend analysis was used to understand the popularity and related events. Content and demographic analyses were conducted to explore potential rationales of people’s stances on each drug.

Results

Time-trend analysis revealed that Hydroxychloroquine and Ivermectin received much more discussion than Molnupiravir and Remdesivir, particularly during COVID-19 surges. Hydroxychloroquine and Ivermectin were highly politicized, related to conspiracy theories, hearsay, celebrity effects, etc. The distribution of stance between the 2 major US political parties was significantly different (P < .001); Republicans were much more likely to support Hydroxychloroquine (+55%) and Ivermectin (+30%) than Democrats. People with healthcare backgrounds tended to oppose Hydroxychloroquine (+7%) more than the general population; in contrast, the general population was more likely to support Ivermectin (+14%).

Conclusion

Our study found that social media users with have different perceptions and stances on off-label versus FDA-authorized drug use across different stages of COVID-19, indicating that health systems, regulatory agencies, and policymakers should design tailored strategies to monitor and reduce misinformation for promoting safe drug use. Our analysis pipeline and stance detection models are made public at https://github.com/ningkko/COVID-drug.

Facilitating Scientific Communication Between Strangers: A Preregistered Lost E-Mail Experiment

Communication scholars are increasingly concerned about biases that shape people's interactions with science. Past study has focused on echo chambers (cultivating social networks that reinforce existing worldviews). People's facilitation of scientific discourse between strangers also may be shaped by their attitudes. To study the latter, we employed a recent adaptation of Milgram's lost letter technique called the lost e-mail technique (LET). We conducted a preregistered field study using a large undergraduate university sample (N = 1,508) to examine how the LET might elucidate people's treatment of scientific information. We distributed four ostensibly misaddressed scientific messages and monitored the likelihood of these e-mails being facilitated by participants. Participants' beliefs about self-esteem's importance, assessed months earlier, were associated with increased facilitation of scientific claims congruent with (vs. incongruent with) these beliefs. Thus, people shape the spread of online information in a manner matching their beliefs, even for people outside their social networks.

Scientific literacy and preferred resources used by Latin American medical students during the COVID-19 pandemic: A multinational survey

Background: This study aims to identify the preferred sources for acquiring knowledge about COVID-19 and to evaluate basic knowledge on critical scientific literature appraisal in students from medical schools located in Spanish speaking countries in Latin America. 

Methods: We designed an online survey of 15 closed-ended questions related to demographics, preferred resources for COVID-19 training, and items to assess critical appraisal skills. A snowball method was used for sampling. We conducted a descriptive analysis and Chi-squared tests to compare the proportion of correct identification of the concept of a preprint and a predatory journal when considering a) self-perceived level of knowledge, b) public vs private school, c) inclusion of a scientific literature appraisal subject in the curriculum, and d) progress in medical school.

Results: Our sample included 770 valid responses, out of which most of the participants included were from Mexico (n=283, 36.8%) and Ecuador (n=229, 29.7%). Participants preferred using evidence-based clinical resources (EBCRs) to learn more about COVID-19 (n=182, 23.6%). The preferred study design was case report/series (n=218, 28.1%). We found that only 265 participants correctly identified the concept of a preprint (34.4%), while 243 students (31.6%) correctly identified the characteristics of a predatory journal. We found no significant differences in the proportion of correct answers regardless of the self-perceived level of knowledge, progress in medical school, or scientific literature critical appraisal classes.

Conclusion: This study is novel in its approach of identifying sources of knowledge used by Latin American medical students and provides insights into the need to reinforce training in critical appraisal of scientific literature during medical school.

Conspiracy theories and social media platforms

Conspiracy theories proliferate online. We provide an overview of information consumption patterns related to conspiracy content on four mainstream social media platforms (Facebook, Twitter, YouTube, and Reddit), with a focus on niche ones. Opinion polarisation and echo chambers appear as pivotal elements of communication around conspiracy theories. A relevant role may also be played by the content moderation policies enforced by each social media platform. Banning contents or users from a social media could lead to a level of user segregation that goes beyond echo chambers and reaches the entire social media space, up to the formation of 'echo platforms'. The insurgence of echo platforms is a new online phenomenon that needs to be investigated as it could foster many dangerous phenomena that we observe online, including the spreading of conspiracy theories.

COVID-19, Vaccination, and Conspiracies: A Micro-Level Qualitative Study in Islamabad, Pakistan

Although vaccination is the only hope to fight against COVID-19, existing vaccine hesitancy is a thought-provoking phenomenon. Significantly, vaccine hesitancy is worsening the situation in Pakistan, leading to an increased number of COVID cases. In this context, this study aims to examine people's perceptions and attitudes towards vaccination. Here the focus was on determining the factors causing disease hesitancy among the masses. The researchers randomly selected a sample of n=17 individuals and gathered data by using telephone interviews and assessed data by using the Interpretive Phenomenological Analysis (IPA) approach. Results revealed that the increased vaccine hesitancy is due to misinformation, conspiracies, myths, and rumors about the side effects of the vaccination. It was also notable that the participants indicated digital media as the primary source of information, showing a potential relationship between social media and misinformation. Also, an intense uncertainty about the healthcare system in Pakistan is hindering the efforts to sustain herd immunity. Thus, due to several myths, rumors, and distrust of the healthcare system, vaccine hesitancy is halting the country's ability to overcome the COVID-19 outbreak. Misinformation is vigorously circulating due to ease of access to different communication platforms, instilling fear of presumed side effects. Hence, the researchers suggest some practical considerations for the government, healthcare workers, and media platforms to counteract the misinformation and increase vaccine acceptance among the masses.

Listening to Voices from African American Communities in the Southern States about COVID-19 Vaccine Information and Communication: A Qualitative Study

The high uptake of COVID-19 vaccines is one of the most promising measures to control the pandemic. However, some African American (AA) communities exhibit vaccination hesitancy due to mis- or disinformation. It is important to understand the challenges in accessing reliable COVID-19 vaccine information and to develop feasible health communication interventions based on voices from AA communities. We conducted 2 focus group discussions (FGDs) among 18 community stakeholders recruited from 3 counties in South Carolina on 8 October and 29 October 2021. The FGDs were conducted online via Zoom meetings. The FGD data were managed and thematically analyzed using NVivo 12. Participants worked primarily in colleges, churches, and health agencies. We found that the challenges of accessing reliable vaccine information in AA communities primarily included structural barriers, information barriers, and a lack of trust. Community stakeholders recommended recruiting trusted messengers, using social events to reach target populations, and conducting health communication campaigns through open dialogue among stakeholders. Health communication interventions directed at COVID-19 vaccine uptake should be grounded in ongoing community engagement, trust-building activities, and transparent communication about vaccine development. Tailoring health communication interventions to different groups may help reduce misinformation spread and thus promote vaccination in AA communities in the southern states.

Cyberchondria, Covid-19 phobia, and well-being: a relational study on teachers

BACKGROUND

This study aims at investigating level and contributor factors of Cyberchondria, Covid-19-related Phobia, and Well-Being in a sample of teachers in Turkey.

METHODS

The study was conducted on teachers (n=1000) working in a province in eastern Turkey. Data for the study were collected using a form that included participants' descriptive characteristics, the Covid-19 Phobia Scale (C19P-SE), the Cyberchondria Severity Scale, and the World Health Organization-5 Well-Being Index (WHO-5). Spearman correlation analysis, Mann-Whitney U test, and Kruskal Wallis analysis of variance were used to analyze the data.

RESULTS

As participant's cyberchondria levels rose, C19P-SE scores increased (r=0.271, p<0.001), and WHO-5 scores decreased (r=-0.224, p<0.05). Corona-phobia was higher in those who used social media than in those who did not (p<0.05). Cyberchondria scale scores were higher among those who had taken medications without a physician's recommendation during the pandemic. Participants who had a disabled person or a person in need of care in their household had higher scores for distrust of the physician and C19P-SE than for the cyberchondria severity scale sub-dimension, and the WHO-5 mean scores were lower (p<0.001, P=0.016, and P=0.020, respectively).

CONCLUSIONS

The study results show that increasing levels of cyberchondria trigger Covid-19 phobias in teachers during the Covid-19 pandemic and negatively affect their well-being. This descriptive study can help understand the risk group for cyberchondria, the influencing factors, and the health and economic consequences, and identify strategies for effective combating with cyberchondria.

Data-driven analytics of COVID-19 'infodemic'

The rampant of COVID-19 infodemic has almost been simultaneous with the outbreak of the pandemic. Many concerted efforts are made to mitigate its negative effect to information credibility and data legitimacy. Existing work mainly focuses on fact-checking algorithms or multi-class labeling models that are less aware of the intrinsic characteristics of the language. Nor is it discussed how such representations can account for the common psycho-socio-behavior of the information consumers. This work takes a data-driven analytical approach to (1) describe the prominent lexical and grammatical features of COVID-19 misinformation; (2) interpret the underlying (psycho-)linguistic triggers in terms of sentiment, power and activity based on the affective control theory; (3) study the feature indexing for anti-infodemic modeling. The results show distinct language generalization patterns of misinformation of favoring evaluative terms and multimedia devices in delivering a negative sentiment. Such appeals are effective to arouse people’s sympathy toward the vulnerable community and foment their spreading behavior.

Sentinel node approach to monitoring online COVID-19 misinformation

Understanding how different online communities engage with COVID-19 misinformation is critical for public health response. For example, misinformation confined to a small, isolated community of users poses a different public health risk than misinformation being consumed by a large population spanning many diverse communities. Here we take a longitudinal approach that leverages tools from network science to study COVID-19 misinformation on Twitter. Our approach provides a means to examine the breadth of misinformation engagement using modest data needs and computational resources. We identify a subset of accounts from different Twitter communities discussing COVID-19, and follow these ‘sentinel nodes’ longitudinally from July 2020 to January 2021. We characterize sentinel nodes in terms of a linked domain preference score, and use a standardized similarity score to examine alignment of tweets within and between communities. We find that media preference is strongly correlated with the amount of misinformation propagated by sentinel nodes. Engagement with sensationalist misinformation topics is largely confined to a cluster of sentinel nodes that includes influential conspiracy theorist accounts. By contrast, misinformation relating to COVID-19 severity generated widespread engagement across multiple communities. Our findings indicate that misinformation downplaying COVID-19 severity is of particular concern for public health response. We conclude that the sentinel node approach can be an effective way to assess breadth and depth of online misinformation penetration.

Impact of Social Media, Extended Parallel Process Model (EPPM) on the Intention to Stay at Home during the COVID-19 Pandemic

Considering how communities perceive the threat and risks of COVID-19, it is essential to examine how emotional regulation stimulated through intrinsic and extrinsic incentive mechanisms via social media can reinforce ‘Stay at home’ intentions. The conceptual framework was developed using the elements of the Extended Parallel Process Model (EPPM). A self-reported questionnaire was used to measure individuals’ intention to stay at home during the pandemic based on the perceived locus of causality as a part of self-determination theory. The empirical research was conducted on a sample of 306 USA respondents. The study results indicate that both components of the EPPM—efficacy and threat—positively affect ‘stay at home’ intentions. Moreover, a positive effect of efficacy on threats was found, as was a moderating effect of threats on the relationship between efficacy and the intention to stay at home. Meanwhile, the influence of social media exposure on threats and behavioral intentions was not significant. People are likely to stay at home as a preventive measure during COVID-19 if there is enough threats and efficacy. However, the abundance of information and opinions in social media can lead to a decreased perceived threat and might disrupt the acceptance of preventive actions.

Combating Misinformation by Sharing the Truth: a Study on the Spread of Fact-Checks on Social Media

Misinformation on social media has become a horrendous problem in our society. Fact-checks on information often fall behind the diffusion of misinformation, which can lead to negative impacts on society. This research studies how different factors may affect the spread of fact-checks over the internet. We collected a dataset of fact-checks in a six-month period and analyzed how they spread on Twitter. The spread of fact-checks is measured by the total retweet count. The factors/variables include the truthfulness rating, topic of information, source credibility, etc. The research identifies truthfulness rating as a significant factor: conclusive fact-checks (either true or false) tend to be shared more than others. In addition, the source credibility, political leaning, and the sharing count also affect the spread of fact-checks. The findings of this research provide practical insights into accelerating the spread of the truth in the battle against misinformation online.

Social Media News Use Induces COVID-19 Vaccine Hesitancy Through Skepticism Regarding Its Efficacy: A Longitudinal Study From the United States

There are mounting concerns about the adverse effects of social media on the public understanding of the COVID-19 pandemic and its potential effects on vaccination coverage. Yet early studies have focused on generic social media use and been based on cross-sectional data limiting any causal inferences. This study is among the first to provide causal support for the speculation that social media news use leads to vaccine hesitancy among US citizens. This two-wave survey study was conducted in the US using Qualtrics online panel-based recruitment. We employ mediation and moderated mediation analyses to test our assumptions. The results suggest that using social media to consume news content can translate into vaccine hesitancy by increasing citizens' skepticism regarding the efficacy of vaccines. However, these effects are contingent upon the news literacy of users, as the effects on vaccine hesitancy are more substantial among those with lower news literacy. The current study recommends to public policymakers and vaccine communication strategists that any attempt to reduce vaccine hesitancy in society should factor in the adverse effects of social media news use that can increase vaccine safety concerns.

How the Italian Twitter Conversation on Vaccines Changed During the First Phase of the Pandemic: A Mixed-Method Analysis

In the context of the European Joint Action on Vaccination, we analyzed, through quantitative and qualitative methods, a random sample of vaccine-related tweets published in Italy between November 2019 and June 2020, with the aim of understanding how the Twitter conversation on vaccines changed during the first phase of the pandemic, compared to the pre-pandemic months. Tweets were analyzed by a multidisciplinary team in terms of kind of vaccine, vaccine stance, tone of voice, population target, mentioned source of information. Multiple correspondence analysis was used to identify variables associated with vaccine stance. We analyzed 2,473 tweets. 58.2% mentioned the COVID-19 vaccine. Most had a discouraging stance (38.1%), followed by promotional (32.5%), neutral (22%) and ambiguous (2.5%). The discouraging stance was the most represented before the pandemic (69.6%). In February and March 2020, discouraging tweets decreased intensely and promotional and neutral tweets dominated the conversation. Between April and June 2020, promotional tweets remained more represented (36.5%), followed by discouraging (30%) and neutral (24.3%). The tweets' tone of voice was mainly polemical/complaining, both for promotional and for discouraging tweets. The multiple correspondence analysis identified a definite profile for discouraging and neutral tweets, compared to promotional and ambiguous tweets. In conclusion, the emergence of SARS-CoV-2 caused a deep change in the vaccination discourse on Twitter in Italy, with an increase of promotional and ambiguous tweets. Systematic monitoring of Twitter and other social media, ideally combined with traditional surveys, would enable us to better understand Italian vaccine hesitancy and plan tailored, data-based communication strategies.

Trusting COVID-19 vaccines as individual and social goal

Trust in vaccines and in the institutions responsible for their management is a key asset in the global response to the COVID-19 pandemic. By means of a structured multi-scales survey based on the socio-cognitive model of trust, this study investigates the interplay of institutional trust, confidence in COVID-19 vaccines, information habits, personal motivations, and background beliefs on the pandemic in determining willingness to vaccinate in a sample of Italian respondents (N = 4096). We observe substantial trust in public institutions and a strong vaccination intention. Theory-driven structural equation analysis revealed what factors act as important predictors of willingness to vaccinate: trust in vaccine manufacturers (which in turn is supported by trust in regulators), collectivist goals, self-perceived knowledgeability, reliance on traditional media for information gathering, and trust in institutional and scientific sources. In contrast, vaccine hesitancy, while confined to a minority, is more prominent in less educated and less affluent respondents. These findings can inform institutional decisions on vaccine communication and vaccination campaigns.

Media Use During the COVID-19 Pandemic: Cross-sectional Study

BACKGROUND

Throughout the pandemic, the general population was encouraged to use media to be kept informed about sanitary measures while staying connected with others to obtain social support. However, due to mixed findings in the literature, it is not clear whether media use in such a context would be pathogenic or salutogenic.

OBJECTIVE

Therefore, the associations between COVID-19-related stressors and frequency of media use for information-seeking on trauma- and stressor-related (TSR) symptoms were examined while also investigating how social media use for support-seeking and peritraumatic distress interact with those variables.

METHODS

A path model was tested in a sample of 5913 adults who completed an online survey.

RESULTS

The number of COVID-19-related stressors (β=.25; P<.001) and extent of information-seeking through media (β=.24; P=.006) were significantly associated with the severity of TSR symptoms in bivariate comparisons. Associations between levels of peritraumatic distress and both COVID-19-related stressors and information-seeking through media, and social media use for support- and information-seeking through media were found (β_{COVID-19 stressors: Peritraumatic Distress Inventory}=.49, P<.001; β_{seeking information: Peritraumatic Distress Inventory}=.70, P<.001; β_{seeking information-seeking support}=.04, P<.001).

CONCLUSIONS

Results suggest that exposure to COVID-19-related stressors and seeking COVID-19-related information through the media are associated with higher levels of peritraumatic distress that, in turn, lead to higher levels of TSR symptoms. Although exposure to the stress of the COVID-19 pandemic may be unavoidable, the frequency of COVID-19-related information consumption through various media should be approached with caution.

Understanding How and by Whom COVID-19 Misinformation is Spread on Social Media: Coding and Network Analyses

Background

During global health crises such as the COVID-19 pandemic, rapid spread of misinformation on social media has occurred. The misinformation associated with COVID-19 has been analyzed, but little attention has been paid to developing a comprehensive analytical framework to study its spread on social media.

Objective

We propose an elaboration likelihood model–based theoretical model to understand the persuasion process of COVID-19–related misinformation on social media.

Methods

The proposed model incorporates the central route feature (content feature) and peripheral features (including creator authority, social proof, and emotion). The central-level COVID-19–related misinformation feature includes five topics: medical information, social issues and people’s livelihoods, government response, epidemic spread, and international issues. First, we created a data set of COVID-19 pandemic–related misinformation based on fact-checking sources and a data set of posts that contained this misinformation on real-world social media. Based on the collected posts, we analyzed the dissemination patterns.

Results

Our data set included 11,450 misinformation posts, with medical misinformation as the largest category (n=5359, 46.80%). Moreover, the results suggest that both the least (4660/11,301, 41.24%) and most (2320/11,301, 20.53%) active users are prone to sharing misinformation. Further, posts related to international topics that have the greatest chance of producing a profound and lasting impact on social media exhibited the highest distribution depth (maximum depth=14) and width (maximum width=2355). Additionally, 97.00% (2364/2437) of the spread was characterized by radiation dissemination.

Conclusions

Our proposed model and findings could help to combat the spread of misinformation by detecting suspicious users and identifying propagation characteristics.

Depolarization of echo chambers by random dynamical nudge

In social networks, users often engage with like-minded peers. This selective exposure to opinions might result in echo chambers, i.e., political fragmentation and social polarization of user interactions. When echo chambers form, opinions have a bimodal distribution with two peaks on opposite sides. In certain issues, where either extreme positions contain a degree of misinformation, neutral consensus is preferable for promoting discourse. In this paper, we use an opinion dynamics model that naturally forms echo chambers in order to find a feedback mechanism that bridges these communities and leads to a neutral consensus. We introduce the random dynamical nudge (RDN), which presents each agent with input from a random selection of other agents’ opinions and does not require surveillance of every person’s opinions. Our computational results in two different models suggest that the RDN leads to a unimodal distribution of opinions centered around the neutral consensus. Furthermore, the RDN is effective both for preventing the formation of echo chambers and also for depolarizing existing echo chambers. Due to the simple and robust nature of the RDN, social media networks might be able to implement a version of this self-feedback mechanism, when appropriate, to prevent the segregation of online communities on complex social issues.