Science vs conspiracy: collective narratives in the age of misinformation

Leveraging artificial intelligence to identify the psychological factors associated with conspiracy theory beliefs online

Given the profound societal impact of conspiracy theories, probing the psychological factors associated with their spread is paramount. Most research lacks large-scale behavioral outcomes, leaving factors related to actual online support for conspiracy theories uncertain. We bridge this gap by combining the psychological self-reports of 2506 Twitter (currently X) users with machine-learning classification of whether the textual data from their 7.7 million social media engagements throughout the pandemic supported six common COVID-19 conspiracy theories. We assess demographic factors, political alignment, factors derived from theory of reasoned action, and individual psychological differences. Here, we show that being older, self-identifying as very left or right on the political spectrum, and believing in false information constitute the most consistent risk factors; denialist tendencies, confidence in one's ability to spot misinformation, and political conservativism are positively associated with support for one conspiracy theory. Combining artificial intelligence analyses of big behavioral data with self-report surveys can effectively identify and validate risk factors for phenomena evident in large-scale online behaviors.

The Roles of Social Media Use and Medical Mistrust in Black Americans' COVID-19 Vaccine Hesitancy: The RISP Model Perspective

The COVID-19 pandemic has brought unprecedented challenges to healthcare and public health messaging in the United States. One area of focus has been vaccination uptake among Black Americans, who have experienced COVID-19 deaths disproportionate to their share of the United States population, raising questions about the processes involved in vaccination perceptions and behaviors. Guided by the Risk Information Seeking and Processing model, this study explored the roles of medical mistrust and social media as a source of risk information in Black Americans' vaccine hesitancy. Survey results from a YouGov panel sample of Black Americans (n = 1,136; 53.5% female) showed that social media use and medical mistrust were positively associated with belief in misinformation related to the COVID-19 vaccine, which, in turn, was positively related to vaccine hesitancy through perceived information insufficiency and information seeking intentions. Furthermore, we found that belief in misinformation and subjective norms toward anti-vaccination also serially mediated the association between social media use and medical mistrust with vaccine hesitancy. Theoretical and practical implications are discussed.

The Relationship Between Social Media Information Sharing Characteristics and Problem Behaviors Among Chinese College Students Under Recommendation Algorithms

Purpose

With the development of information technology and various social media, recommendation algorithms have increasingly more influence on users' social media usage. To date, there has been limited research focused on analyzing the impact of recommendation algorithms on social media use and their corresponding role in the development of problematic behaviors. The present study analyzes the impact of recommendation algorithms on college students' information sharing and internalizing, externalizing problem behaviors to address the aforementioned shortcomings.

Methods

An online questionnaire survey was conducted among 34,752 college students in China. A latent profile analysis was conducted to explore the various behavioral patterns of Chinese college students' information sharing across the three social media platforms identified for this study. The Bolck-Croon-Hagenaars (BCH) method Regression Mixture Modeling was then used to analyze the differences in internalizing and externalizing problem behaviors among the different subgroups of Chinese college students.

Results

The level of information sharing by college students across different social media platforms could be divided into "WeChat Moments low-frequency information sharing", "middle-frequency comprehensive information sharing", "TikTok high-frequency information sharing", and "Sina Weibo high-frequency information sharing". Significant differences were observed regarding internalizing and externalizing problem behaviors among college students in different information-sharing subgroups.

Conclusion

This study identified four subgroups with different information-sharing characteristics using latent profile analysis. Among them, college students who are in subgroup of social media information sharing influenced by recommendation algorithms exhibit higher frequency of information sharing and higher level of internalizing and externalizing problematic behaviors. These results expand our understanding of college students' social media usage and problem behaviors from a technological perspective. In future, the negative impacts of recommendation algorithms on college students can be reduced by improving their awareness of these algorithms and optimizing the algorithms themselves.

Followers do not dictate the virality of news outlets on social media

Initially conceived for entertainment, social media platforms have profoundly transformed the dissemination of information and consequently reshaped the dynamics of agenda-setting. In this scenario, understanding the factors that capture audience attention and drive viral content is crucial. Employing Gibrat's Law, which posits that an entity's growth rate is unrelated to its size, we examine the engagement growth dynamics of news outlets on social media. Our analysis includes the Facebook historical data of over a thousand news outlets, encompassing approximately 57 million posts in four European languages from 2008 to the end of 2022. We discover universal growth dynamics according to which news virality is independent of the traditional size of the outlet. Moreover, our analysis reveals a significant long-term impact of news source reliability on engagement growth, with engagement induced by unreliable sources decreasing over time. We conclude the article by presenting a statistical model replicating the observed growth dynamics.

People see what they want to see: an EEG study

This study explored selective exposure and confirmation bias in the choices participants made about which political videos to watch, and whether their political positions changed after they watched videos that either agreed with or opposed their positions on two controversial issues in South Korea: North Korea policy and social welfare policy. The participants completed questionnaires before and after they watched the videos, were asked to select thumbnails of videos before they watched any, and had their brain wave activity measured through electroencephalogram (EEG) as they watched both types of videos. The participants demonstrated selective exposure as they primarily selected video thumbnails with content that matched their political orientations, and they demonstrated confirmation bias as their questionnaire responses after they watched the videos indicated that their positions had hardened. There were also statistically significant differences in alpha, beta, sensory motor rhythm, low beta, mid beta, and fast alpha activity depending on the political orientation consistency between the participants and the videos. Future studies could expand this line of research beyond college students and beyond Asia, and longitudinal work could also be conducted to determine if the obtained patterns remain constant over time.

Persistent interaction patterns across social media platforms and over time

Growing concern surrounds the impact of social media platforms on public discourse1-4 and their influence on social dynamics5-9, especially in the context of toxicity10-12. Here, to better understand these phenomena, we use a comparative approach to isolate human behavioural patterns across multiple social media platforms. In particular, we analyse conversations in different online communities, focusing on identifying consistent patterns of toxic content. Drawing from an extensive dataset that spans eight platforms over 34 years-from Usenet to contemporary social media-our findings show consistent conversation patterns and user behaviour, irrespective of the platform, topic or time. Notably, although long conversations consistently exhibit higher toxicity, toxic language does not invariably discourage people from participating in a conversation, and toxicity does not necessarily escalate as discussions evolve. Our analysis suggests that debates and contrasting sentiments among users significantly contribute to more intense and hostile discussions. Moreover, the persistence of these patterns across three decades, despite changes in platforms and societal norms, underscores the pivotal role of human behaviour in shaping online discourse.

Cross-platform social dynamics: an analysis of ChatGPT and COVID-19 vaccine conversations

The role of social media in information dissemination and agenda-setting has significantly expanded in recent years. By offering real-time interactions, online platforms have become invaluable tools for studying societal responses to significant events as they unfold. However, online reactions to external developments are influenced by various factors, including the nature of the event and the online environment. This study examines the dynamics of public discourse on digital platforms to shed light on this issue. We analyzed over 12 million posts and news articles related to two significant events: the release of ChatGPT in 2022 and the global discussions about COVID-19 vaccines in 2021. Data was collected from multiple platforms, including Twitter, Facebook, Instagram, Reddit, YouTube, and GDELT. We employed topic modeling techniques to uncover the distinct thematic emphases on each platform, which reflect their specific features and target audiences. Additionally, sentiment analysis revealed various public perceptions regarding the topics studied. Lastly, we compared the evolution of engagement across platforms, unveiling unique patterns for the same topic. Notably, discussions about COVID-19 vaccines spread more rapidly due to the immediacy of the subject, while discussions about ChatGPT, despite its technological importance, propagated more gradually.

Predictors of young people's anti-vaccine attitudes in the context of the COVID-19 pandemic

The objective of this research was to explore how attitudes to science and scientists may be related to anti-vax positions and whether the psychological trait known as Need for Closure may influence the relationship between any or all of these attitudes. A questionnaire was administered to a sample of 1128 young people aged 18-25 living in Italy during the COVID-19 health crisis. Based on the results of exploratory and confirmatory factor analyses, which allowed the extraction of a three-factor solution (scepticism about science, unrealistic expectations about science and anti-vax postures), we tested our hypotheses by means of a structural equation model. We found that anti-vax positions are strongly correlated with sceptical views of science, while unrealistic expectations about science affect attitudes to vaccination only indirectly. Either way, Need for Closure emerged as a key variable in our model, as it significantly moderates the effect of both factors on anti-vax positions.

Ayahuasca: A review of historical, pharmacological, and therapeutic aspects

Ayahuasca is a psychedelic plant brew originating from the Amazon rainforest. It is formed from two basic components, the Banisteriopsis caapi vine and a plant containing the potent psychedelic dimethyltryptamine (DMT), usually Psychotria viridis. Here we review the history of ayahuasca and describe recent work on its pharmacology, phenomenological responses, and clinical applications. There has been a significant increase in interest in ayahuasca since the turn of the millennium. Anecdotal evidence varies significantly, ranging from evangelical accounts to horror stories involving physical and psychological harm. The effects of the brew on personality and mental health outcomes are discussed in this review. Furthermore, phenomenological analyses of the ayahuasca experience are explored. Ayahuasca is a promising psychedelic agent that warrants greater empirical attention regarding its basic neurochemical mechanisms of action and potential therapeutic application.

A multilayered graph-based framework to explore behavioural phenomena in social media conversations

OBJECTIVE

Social media is part of current health communications. This research aims to delve into the effects of social contagion, biased assimilation, and homophily in building and changing health opinions on social media.

MATERIALS AND METHODS

Conversations about COVID-19 vaccination on English and Spanish Twitter are the case studies. A new multilayered graph-based framework supports the integrated analysis of content similarity within and across posts, users, and conversations to interpret contrasting and confluent user stances. Deep learning models are applied to infer stance. Graph centrality and homophily scores support the interpretation of information reproduction.

RESULTS

The results show that semantically related English posts tend to present a similar stance about COVID-19 vaccination (rstance = 0.51) whereas Spanish posts are more heterophilic (rstance = 0.38). Neither case showed evidence of homophily regarding user influence or vaccine hashtags. Graph filters for Pfizer and Astrazeneca with a similarity threshold of 0.85 show stance homophily in English scenarios (i.e. rstance = 0.45 and rstance = 0.58, respectively) and small homophily in Spanish scenarios (i.e. r = 0.12 and r = 0.3, respectively). Highly connected users are a minority and are not socially influential. Spanish conversations showed stance homophily, i.e. most of the connected conversations promote vaccination (rstance = 0.42), whereas English conversations are more likely to offer contrasting stances.

CONCLUSION

The methodology proposed for quantifying the impact of natural and intentional social behaviours in health information reproduction can be applied to any of the main social platforms and any given topic of conversation. Its effectiveness was demonstrated by two case studies describing English and Spanish demographic and sociocultural scenarios.

(Why) Is Misinformation a Problem?

In the last decade there has been a proliferation of research on misinformation. One important aspect of this work that receives less attention than it should is exactly why misinformation is a problem. To adequately address this question, we must first look to its speculated causes and effects. We examined different disciplines (computer science, economics, history, information science, journalism, law, media, politics, philosophy, psychology, sociology) that investigate misinformation. The consensus view points to advancements in information technology (e.g., the Internet, social media) as a main cause of the proliferation and increasing impact of misinformation, with a variety of illustrations of the effects. We critically analyzed both issues. As to the effects, misbehaviors are not yet reliably demonstrated empirically to be the outcome of misinformation; correlation as causation may have a hand in that perception. As to the cause, advancements in information technologies enable, as well as reveal, multitudes of interactions that represent significant deviations from ground truths through people's new way of knowing (intersubjectivity). This, we argue, is illusionary when understood in light of historical epistemology. Both doubts we raise are used to consider the cost to established norms of liberal democracy that come from efforts to target the problem of misinformation.

COVID-19 Vaccine Hesitancy in Cameroon: The Role of Medical Mistrust and Social Media Use

Most African countries report low COVID-19 vaccination rates (Msellati et al., 2022; WHO Africa; 2020). This study focuses on factors associated with vaccine hesitancy specifically in the country of Cameroon. Social media use and medical mistrust have been suggested as key variables that may increase vaccine hesitancy. Adopting the information-related perspective guided by the risk information seeking and processing model, the current research explored how social media use and medical mistrust are related to vaccine hesitancy among Cameroonians. Survey results from a sample of 1,000 Cameroonians fielded in early 2022 showed that social media use and medical mistrust were positively associated with belief in misinformation related to the COVID-19 vaccine. Belief in misinformation about the COVID-19 vaccine was negatively associated with perceived information insufficiency. A positive relationship between perceived information insufficiency and information seeking, as well as a negative relationship between information seeking and vaccine hesitancy were also found. Theoretical and practical implications are discussed.

Between alternative and traditional social platforms: the case of gab in exploring the narratives on the pandemic and vaccines

The phenomenon of deplatforming intended as the removal of social media accounts because of breaking rules on mainstream platforms such as Facebook, Twitter, YouTube, and Instagram recently increased due to new terms and conditions of use of digital media, and new alternative social media platforms emerged and presented themselves as protectors of freedom expression. In this way, it becomes interesting to understand better the context of these platforms' so-called web suburbs that consist in those digital places that ≪host what we can generally call "subcultures," including fandoms, religious sects, political extremists, and subcultures≫. Since April 2020, Gab can be considered the most widespread alternative platform in Western countries, with twenty million users daily, born as Twitter and Facebook alternative social media. The alternative social media platforms are intended as other connection services between users, which is halfway between a social media and a discussion forum born to boycott the censorship actions of the main social media platforms (Meta Group, Twitter, etc.) and celebrate free speech even on controversial positions. How are sensitive topics, such as the one that concerns the skepticism related to the approvals of vaccines during the pandemic, addressed on the alternative social media platform compared to how they are dealt with on the mainstream social media platforms? This explorative work wonders about the users' points of view on vaccine concerns and the relevant differences between Gab and Facebook in addressing this topic. The empirical part of this work has been set starting from the dataset composed of Gab and Facebook content posted between March 2020 and July 2021. The posts were extracted with web scraping techniques (for Gab) and proprietary data tools (for Facebook), querying the keywords: vaccine, vaccines, anti-vax (no-vax), Covid, Covid-19, coronavirus. The collection procedure considered the different platforms' structure and their different organization of the interaction spaces. The population consisted of 8000 English writers' posts, from which 2000 posts with the highest interaction value were extracted. The dataset was analyzed using Topic Modeling, Factor, and Classification Analysis techniques. Our work's methodological output deals with comparing these social media platforms, bearing in mind their ontological objects and their algorithms' role. From the analysis emerged the differences and similarities of the social media platforms in terms of the type of content published, rates of involvement, sources of information, and directions of the considered speech. These differences have been duly highlighted by three clusters related to discourse orientation and communication approach: Conflict of views, Emotional externalization, Recommendation and practices. In addition to the type of communication and information circulating on a powerful platform such as Gab, the results help us understand the different narratives promoted on the two social media platforms and their role in the possible promotion of the same sentiment, opinions, and ideological polarization.

The Influence of News Consumption Habits and Dispositional Traits on Trust in Medical Scientists

Public trust in medical institutions is essential for ensuring compliance with medical directives. However, the politicization of public health issues and the polarized nature of major news outlets suggest that partisanship and news consumption habits can influence medical trust. This study employed a survey with 858 participants and used regression analysis to assesses how news consumption habits and information assessment traits (IATs) influence trust in medical scientists. IATs included were conscientiousness, openness, need for cognitive closure (NFCC), and cognitive reflective thinking (CRT). News sources were classified on the basis of factuality and political bias. Initially, readership of liberally biased news was positively associated with medical trust (p < 0.05). However, this association disappeared when controlling for the news source's factuality (p = 0.28), while CRT (p < 0.05) was positively associated with medical trust. When controlling for conservatively biased news sources, factuality of the news source (p < 0.05) and NFCC (p < 0.05) were positively associated with medical trust. While partisan media bias may influence medical trust, these results suggest that those who have higher abilities to assess information and who prefer more credible news sources have a greater trust in medical scientists.

Barriers to COVID-19 vaccine acceptance to improve messages for vaccine uptake in indigenous populations in the central highlands of Guatemala: a participatory qualitative study

INTRODUCTION

As of July 2022, a little over one-third of Guatemalans were fully vaccinated. While COVID-19 vaccination rates are not officially reported nationally by racial/ethnic groups, non-governmental organisations and reporters have observed that COVID-19 vaccination rates are especially low among high-risk Indigenous populations. We conducted one of the first studies on COVID-19 vaccine acceptance in Indigenous populations in the Central Highlands of Guatemala, which aimed to better understand the barriers to COVID-19 vaccine uptake and how to improve vaccine promotional campaigns.

METHODS

In November 2021, we conducted eight focus group discussions (FGDs) with 42 Indigenous men and women and 16 in-depth interviews (IDIs) with community health workers, nurses and physicians in Chimaltenango and Sololá. Using a participatory design approach, our qualitative analysis used constant comparative methods to understand the inductive and deductive themes from the FGD and IDI transcripts.

RESULTS

We found three major overarching barriers to vaccination within the sampled population: (1) a lack of available easily understandable, linguistically appropriate and culturally sensitive COVID-19 vaccine information; (2) vaccine access and supply issues that prevented people from being vaccinated efficiently and quickly; and (3) widespread misinformation and disinformation that prey on people's fears of the unknown and mistrust of the medical establishment and government.

CONCLUSION

When developing COVID-19 vaccine messages, content should be culturally relevant, appropriate for low-literacy populations and in the languages that people prefer to speak. Promotional materials should be in multiple modalities (print, radio and social media) and also have specific Maya cultural references (dress, food and concepts of disease) to ensure messaging connects with intended targets. This study supports the need for more robust research into best practices for communicating about COVID-19 vaccines to marginalised communities globally and suggests that policy makers should invest in targeted local solutions to increase vaccine uptake.

What Are Conspiracy Theories? A Definitional Approach to Their Correlates, Consequences, and Communication

Conspiracy theories are abundant in social and political discourse, with serious consequences for individuals, groups, and societies. However, psychological scientists have started paying close attention to them only in the past 20 years. We review the spectacular progress that has since been made and some of the limitations of research so far, and we consider the prospects for further progress. To this end, we take a step back to analyze the defining features that make conspiracy theories different in kind from other beliefs and different in degree from each other. We consider how these features determine the adoption, consequences, and transmission of belief in conspiracy theories, even though their role as causal or moderating variables has seldom been examined. We therefore advocate for a research agenda in the study of conspiracy theories that starts-as is routine in fields such as virology and toxicology-with a robust descriptive analysis of the ontology of the entity at its center.

Everything causes cancer? Beliefs and attitudes towards cancer prevention among anti-vaxxers, flat earthers, and reptilian conspiracists: online cross sectional survey

OBJECTIVE

To evaluate, using an online non-probability sample, the beliefs about and attitudes towards cancer prevention of people professing vaccination scepticism or conspiracy theories.

DESIGN

Cross sectional survey.

SETTING

Data collected mainly from ForoCoches (a well known Spanish forum) and other platforms, including Reddit (English), 4Chan (English), HispaChan (Spanish), and a Spanish language website for cancer prevention (mejorsincancer.org) from January to March 2022.

PARTICIPANTS

Among 1494 responders, 209 were unvaccinated against covid-19, 112 preferred alternative rather than conventional medicine, and 62 reported flat earth or reptilian beliefs.

MAIN OUTCOME MEASURES

Cancer beliefs assessed using the Cancer Awareness Measure (CAM) and Cancer Awareness Measure Mythical Causes Scale (CAM-MYCS) (both validated tools).

RESULTS

Awareness of the actual causes of cancer was greater (median CAM score 63.6%) than that of mythical causes (41.7%). The most endorsed mythical causes of cancer were eating food containing additives or sweeteners, feeling stressed, and eating genetically modified food. Awareness of the actual and mythical causes of cancer among the unvaccinated, alternative medicine, and conspiracy groups was lower than among their counterparts. A median of 54.5% of the actual causes was accurately identified among each of the unvaccinated, alternative medicine, and conspiracy groups, and a median of 63.6% was identified in each of the three corresponding counterparts (P=0.13, 0.04, and 0.003, respectively). For mythical causes, medians of 25.0%, 16.7%, and 16.7% were accurately identified in the unvaccinated, alternative medicine, and conspiracy groups, respectively; a median of 41.7% was identified in each of the three corresponding counterparts (P<0.001 in adjusted models for all comparisons). In total, 673 (45.0%) participants agreed with the statement "It seems like everything causes cancer." No significant differences were observed among the unvaccinated (44.0%), conspiracist (41.9%), or alternative medicine groups (35.7%), compared with their counterparts (45.2%, 45.7%, and 45.8%, respectively).

CONCLUSIONS

Almost half of the participants agreed that "It seems like everything causes cancer," which highlights the difficulty that society encounters in differentiating actual and mythical causes owing to mass information. People who believed in conspiracies, rejected the covid-19 vaccine, or preferred alternative medicine were more likely to endorse the mythical causes of cancer than their counterparts but were less likely to endorse the actual causes of cancer. These results suggest a direct connection between digital misinformation and consequent erroneous health decisions, which may represent a further preventable fraction of cancer.

Balanced Reporting and Boomerang Effect: An Analysis of Croatian Online News Sites Vaccination Coverage and User Comments during the COVID-19 Pandemic

The purpose of this paper was to explore online media coverage of COVID-19 vaccination and user reactions to the different types of coverage. The authors aimed to investigate possible boomerang effects that arise when COVID-19 media coverage is assertive and confident, and to determine the effects of balanced reporting. A two-stage random sample comprised a total of 300 articles published in three Croatian online news sites during a period from 1 February 2020, through 15 January 2022. The data were categorized using human coding content analysis, while reliability of coding was checked by using two coders and calculating reliability coefficients. The data were analyzed by means of negative binomial regression analysis. The results revealed that COVID-19 reporting was mainly consensual, i.e., it provided largely affirmative information about vaccines. However, user comments were highly polarized and mostly negative, with the majority of anti-vaccination tropes linked to the "corrupt elites". Based on the user comments, the negative influence of balanced reporting on COVID-19 vaccines and the existence of boomerang effect in cases of the overtly persuasive affirmative reporting was also established. The boomerang effect did not depend on the context, i.e., on the type of reporting. This study extends previous research on balanced reporting and boomerang effects by analyzing online comments as a potentially good parallelism of the offline discursive strategies of the pro-vaccination and anti-vaccination communication. The results of the study can be used for the adjustment of strategic communication targeting the vaccine hesitant audience. Based on the study results, it is recommended that relativization and politicization of science should be prevented by not equating scientific consensus with absolute epistemological certainty and by addressing legitimate concerns of vaccine hesitant persons without putting explicit blame on them.

Opinion Leaders and Structural Hole Spanners Influencing Echo Chambers in Discussions About COVID-19 Vaccines on Social Media in China: Network Analysis

BACKGROUND

Social media provide an ideal medium for breeding and reinforcing vaccine hesitancy, especially during public health emergencies. Algorithmic recommendation-based technology along with users' selective exposure and group pressure lead to online echo chambers, causing inefficiency in vaccination promotion. Avoiding or breaking echo chambers largely relies on key users' behavior.

OBJECTIVE

With the ultimate goal of eliminating the impact of echo chambers related to vaccine hesitancy on social media during public health emergencies, the aim of this study was to develop a framework to quantify the echo chamber effect in users' topic selection and attitude contagion about COVID-19 vaccines or vaccinations; detect online opinion leaders and structural hole spanners based on network attributes; and explore the relationships of their behavior patterns and network locations, as well as the relationships of network locations and impact on topic-based and attitude-based echo chambers.

METHODS

We called the Sina Weibo application programming interface to crawl tweets related to the COVID-19 vaccine or vaccination and user information on Weibo, a Chinese social media platform. Adopting social network analysis, we examined the low echo chamber effect based on topics in representational networks of information, according to attitude in communication flow networks of users under different interactive mechanisms (retweeting, commenting). Statistical and visual analyses were used to characterize behavior patterns of key users (opinion leaders, structural hole spanners), and to explore their function in avoiding or breaking topic-based and attitude-based echo chambers.

RESULTS

Users showed a low echo chamber effect in vaccine-related topic selection and attitude interaction. For the former, the homophily was more obvious in retweeting than in commenting, whereas the opposite trend was found for the latter. Speakers, replicators, and monologists tended to be opinion leaders, whereas common users, retweeters, and networkers tended to be structural hole spanners. Both leaders and spanners tended to be "bridgers" to disseminate diverse topics and communicate with users holding cross-cutting attitudes toward COVID-19 vaccines. Moreover, users who tended to echo a single topic could bridge multiple attitudes, while users who focused on diverse topics also tended to serve as bridgers for different attitudes.

CONCLUSIONS

This study not only revealed a low echo chamber effect in vaccine hesitancy, but further elucidated the underlying reasons from the perspective of users, offering insights for research about the form, degree, and formation of echo chambers, along with depolarization, social capital, stakeholder theory, user portraits, dissemination pattern of topic, and sentiment. Therefore, this work can help to provide strategies for public health and public opinion managers to cooperate toward avoiding or correcting echo chamber chaos and effectively promoting online vaccine campaigns.

Science vs. Conspiracy Theory about COVID-19: Need for Cognition and Openness to Experience Increased Belief in Conspiracy-Theoretical Postings on Social Media

In the context of COVID-19 virus containment, there is a lack of acceptance of preventive measures in the population. The present work investigated which factors influence the belief in scientific propositions compared with belief in conspiracy theories. The focus here was on the determinants of conspiracy beliefs in the context of COVID-19 related media content. Using an online questionnaire (N = 175), results indicate that scientific compared to conspiracy-theoretical media content led to higher acceptance. Furthermore, need for cognition (NFC-K), a conspiracy-theoretical worldview (CMQ), and openness to experience (NEO-FFI) were positively associated with conspiracy beliefs derived from Facebook postings. In addition, a conspiracy-theoretical worldview was negatively associated with belief in scientific media content. Furthermore, agreeableness was unrelated to conspiracy beliefs, although it was positively associated with conspiracy-theoretical worldview. The results imply promising persuasion strategies for reducing conspiracy-theoretical beliefs and to increase the acceptance of preventive measures.

Should I trust or should I go? How people perceive and assess the quality of science communication to avoid fake news

This paper investigates how citizens of five European countries (Italy, Poland, Portugal, Slovakia, and Spain) enquire about scientific issues, how they rate scientific information on climate change and vaccines in terms of quantity and quality, and their strategies for overcoming perceived defects. We conducted a public consultation involving almost 500 citizens and addressed controversial science-related topics. Discussions were qualitatively content analyzed. The public consultations revealed the prevalence of traditional media as a source of scientific information, and the results presented a general perception of inadequate, imprecise, and insufficient scientific communication. Finally, we show how traditional media are still the most frequently used channels and that personal criteria prevail in the evaluation of the reliability of information sources.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11135-022-01569-5.

Digital Information Approach through Social Media among Gen Z and Millennials: The Global Scenario during the COVID-19 Pandemic

An infodemic represents a concern for public health, influencing the general population's perceptions of key health issues. Misinformation is rapidly spread by social media, particularly among young generations. We used data from the WHO "Social Media and COVID-19" study, which was conducted in 24 countries worldwide on over 23,000 subjects aged 18-40 years, to explore Generation Z and Millennials' models for health-information-seeking behaviors on social media. We summarized data on the most used sources of information, content of interest, and content sharing, as well as the sentiment toward the infodemic, through descriptive statistics and Chi-square test to verify the differences between groups. Among the survey respondents, 9475 (40.3%) were from high-income countries (HIC), 8000 (34.1%) from upper-middle-income countries (UMIC), and 6007 (25.6%) from lower-middle-income countries (LMIC). Social media were the most used sources of information to retrieve news on COVID-19 disease (about 79% in HIC, 87% in UMIC, and 90% in LIC) and the COVID-19 vaccine (about 78% in HIC and about 88% in UMIC and LIC). More than a half of the young respondents declared that they pay attention to scientific contents (about 51% in HIC, 59% in UMIC, and 55% in LMIC). Finally, most young participants reported feeling overwhelmed by the infodemic. However, this sentiment did not stop them from seeking information about COVID-19. Our findings highlight the importance of shaping public health interventions and campaigns on social media platforms and leveraging scientific contents. Public health authorities should work also on strategies to improve the digital literacy of the population as a driving force to empower them and achieve better health outcomes.

The (im-)moral scientist? Measurement and framing effects shape the association between scientists and immorality

Recent years have not only seen growing public distrust in science, but also in the people conducting science. Yet, attitudes toward scientists remain largely unexplored, and the limited body of literature that exists points to an interesting ambivalence. While survey data suggest scientists to be positively evaluated (e.g., respected and trusted), research has found scientists to be perceived as capable of immoral behavior. We report two experiments aimed at identifying what contributes to this ambivalence through systematic investigations of stereotypical perceptions of scientists. In these studies, we particularly focus on two potential sources of inconsistencies in previous work: divergent operationalizations of morality (measurement effects), and different specifications of the broad group of scientists (framing effects). Results show that scientists are generally perceived as more likely to violate binding as opposed to individualizing moral foundations, and that they deviate from control groups more strongly on the latter. The extent to which different morality measures reflect the differentiation between binding and individualizing moral foundations at least partially accounts for previous contradictory findings. Moreover, the results indicate large variation in perceptions of different types of scientists: people hold more positive attitudes toward university-affiliated scientists as compared to industry-affiliated scientists, with perceptions of the 'typical scientist' more closely resembling the latter. Taken together, the findings have important academic ramifications for science skepticism, morality, and stereotyping research as well as valuable practical implications for successful science communication.

Hate Speech in a Telegram Conspiracy Channel During the First Year of the COVID-19 Pandemic

Research has explored how the COVID-19 pandemic triggered a wave of conspiratorial thinking and online hate speech, but little is empirically known about how different phases of the pandemic are associated with hate speech against adversaries identified by online conspiracy communities. This study addresses this gap by combining observational methods with exploratory automated text analysis of content from an Italian-themed conspiracy channel on Telegram during the first year of the pandemic. We found that, before the first lockdown in early 2020, the primary target of hate was China, which was blamed for a new bioweapon. Yet over the course of 2020 and particularly after the beginning of the second lockdown, the primary targets became journalists and healthcare workers, who were blamed for exaggerating the threat of COVID-19. This study advances our understanding of the association between hate speech and a complex and protracted event like the COVID-19 pandemic, and it suggests that country-specific responses to the virus (e.g., lockdowns and re-openings) are associated with online hate speech against different adversaries depending on the social and political context.

Losing the battle over best-science guidance early in a crisis: COVID-19 and beyond

Ensuring widespread public exposure to best-science guidance is crucial in any crisis, e.g., coronavirus disease 2019 (COVID-19), monkeypox, abortion misinformation, climate change, and beyond. We show how this battle got lost on Facebook very early during the COVID-19 pandemic and why the mainstream majority, including many parenting communities, had already moved closer to more extreme communities by the time vaccines arrived. Hidden heterogeneities in terms of who was talking and listening to whom explain why Facebook's own promotion of best-science guidance also appears to have missed key audience segments. A simple mathematical model reproduces the exposure dynamics at the system level. Our findings could be used to tailor guidance at scale while accounting for individual diversity and to help predict tipping point behavior and system-level responses to interventions in future crises.

Conspiracy theories in digital environments: Moving the research field forward

In the past few years, the discussion of conspiracy theories has embroiled researchers, politicians and the public alike. During the COVID-19 pandemic in particular, the term 'conspiracy theory' became a buzzword in the news media, public communication and everyday discussions. The pandemic also demonstrated that conspiratorial narratives disseminated online are not benign, obscure and eventually harmless ideas, but can mislead policy making, hinder crisis relief and public health efforts, or undermine trust in institutions and science. Factors contributing to the prevalence of conspiracy theories are complex and include psychological as well as socio-political factors. This special issue focuses specifically on the role of digital media and how they shape the dissemination and mitigation of, as well as research on, conspiracy theories. The special issue includes 13 research articles by authors from 11 countries and regions, which provide timely insights into the phenomenon of conspiracy theories with cross-cultural and cross-platform advances.

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.

Have beliefs in conspiracy theories increased over time?

The public is convinced that beliefs in conspiracy theories are increasing, and many scholars, journalists, and policymakers agree. Given the associations between conspiracy theories and many non-normative tendencies, lawmakers have called for policies to address these increases. However, little evidence has been provided to demonstrate that beliefs in conspiracy theories have, in fact, increased over time. We address this evidentiary gap. Study 1 investigates change in the proportion of Americans believing 46 conspiracy theories; our observations in some instances span half a century. Study 2 examines change in the proportion of individuals across six European countries believing six conspiracy theories. Study 3 traces beliefs about which groups are conspiring against “us,” while Study 4 tracks generalized conspiracy thinking in the U.S. from 2012 to 2021. In no instance do we observe systematic evidence for an increase in conspiracism, however operationalized. We discuss the theoretical and policy implications of our findings.

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.

Understanding the spread of COVID-19 misinformation on social media: The effects of topics and a political leader's nudge

The spread of misinformation on social media has become a major societal issue during recent years. In this work, we used the ongoing COVID-19 pandemic as a case study to systematically investigate factors associated with the spread of multi-topic misinformation related to one event on social media based on the heuristic-systematic model. Among factors related to systematic processing of information, we discovered that the topics of a misinformation story matter, with conspiracy theories being the most likely to be retweeted. As for factors related to heuristic processing of information, such as when citizens look up to their leaders during such a crisis, our results demonstrated that behaviors of a political leader, former US President Donald J. Trump, may have nudged people's sharing of COVID-19 misinformation. Outcomes of this study help social media platform and users better understand and prevent the spread of misinformation on social media.

Exploring the Association between Misinformation Endorsement, Opinions on the Government Response, Risk Perception, and COVID-19 Vaccine Hesitancy in the US, Canada, and Italy

The COVID-19 pandemic has highlighted the adverse consequences created by an infodemic, specifically bringing attention to compliance with public health guidance and vaccine uptake. COVID-19 vaccine hesitancy is a complex construct that is related to health beliefs, misinformation exposure, and perceptions of governmental institutions. This study draws on theoretical models and current data on the COVID-19 infodemic to explore the association between the perceived risk of COVID-19, level of misinformation endorsement, and opinions about the government response on vaccine uptake. We surveyed a sample of 2697 respondents from the US, Canada, and Italy using a mobile platform between 21–28 May 2021. Using multivariate regression, we found that country of residence, risk perception of contracting and spreading COVID-19, perception of government response and transparency, and misinformation endorsement were associated with the odds of vaccine hesitancy. Higher perceived risk was associated with lower odds of hesitancy, while lower perceptions of government response and higher misinformation endorsement were associated with higher hesitancy.

Link Prediction in Time Varying Social Networks

Predicting new links in complex networks can have a large societal impact. In fact, many complex systems can be modeled through networks, and the meaning of the links depend on the system itself. For instance, in social networks, where the nodes are users, links represent relationships (such as acquaintance, friendship, etc.), whereas in information spreading networks, nodes are users and content and links represent interactions, diffusion, etc. However, while many approaches involve machine learning-based algorithms, just the most recent ones account for the topology of the network, e.g., geometric deep learning techniques to learn on graphs, and most of them do not account for the temporal dynamics in the network but train on snapshots of the system at a given time. In this paper, we aim to explore Temporal Graph Networks (TGN), a Graph Representation Learning-based approach that natively supports dynamic graphs and assigns to each event (link) a timestamp. In particular, we investigate how the TGN behaves when trained under different temporal granularity or with various event aggregation techniques when learning the inductive and transductive link prediction problem on real social networks such as Twitter, Wikipedia, Yelp, and Reddit. We find that initial setup affects the temporal granularity of the data, but the impact depends on the specific social network. For instance, we note that the train batch size has a strong impact on Twitter, Wikipedia, and Yelp, while it does not matter on Reddit.

Unlocking Conspiracy Belief Systems: How Fact-Checking Label on Twitter Counters Conspiratorial MMR Vaccine Misinformation

This study tested whether a simple fact-checking label on Twitter effectively reduces vaccine conspiracy beliefs, misinformation engagement intentions, and vaccination intentions. A web-based experiment (N = 206) of adults living in the United States through Amazon Mechanical Turk (MTurk) was conducted for the measles-mumps-rubella (MMR) vaccine in March 2020. The results showed that the fact-checking label attached to the conspiratorial misinformation post significantly reduced MMR vaccine conspiracy beliefs compared to the no fact-checking (misinformation-only) condition but did not directly affect MMR misinformation engagement intentions and MMR vaccination intentions. In addition, we found that the fact-checking label effectively decreased vaccine conspiracy beliefs and misinformation engagement intentions for those whose prior favorable attitudes toward MMR vaccination were relatively low. Based on our findings, we suggest that public health professionals and health communicators use the fact-checking label as a promising tool for countering conspiracy theories about vaccination. However, they should further seek alternative ways to limit the public's engagement in misinformation-related activities on social media and promote health protective behavioral intentions, given the limited effects of fact-checking labels.

Evaluating the benefits and risks of social media for wildlife conservation

Given its extensive volume and reach, social media has the potential to widely spread conservation messaging and be a powerful tool to mobilize social change for conserving biodiversity. We synthesized gray and primary academic literature to investigate the effects of social media on wildlife conservation, revealing several overarching benefits and risks. We found that social media can increase pro-conservation behaviours among the public, increase conservation funding, and incite policy changes. Conversely, social media can contribute to species exploitation and illegal trade, cause unprecedented increases in tourism in protected areas, and perpetuate anti-conservation behaviours via misinformation. In most cases, we found that content sharing on social media did not result in a detectable impact on conservation; in this paper, however, we focus on providing examples where conservation impact was achieved. We relate these positive and negative outcomes of social media to psychological phenomena that may influence conservation efforts and discuss limitations of our findings. We conclude with recommendations of best practices to social media administrators, public social media users, nongovernmental organizations, and governing agencies to minimize conservation risks while maximizing beneficial outcomes. By improving messaging, policing online misconduct, and providing guidance for action, social media can help achieve wildlife conservation goals.

Climate Change Disinformation and How to Combat It

Climate change presents a challenge at multiple levels: It challenges our cognitive abilities because the effect of the accumulation of emissions is difficult to understand. Climate change also challenges many people's worldview because any climate mitigation regime will have economic and political implications that are incompatible with libertarian ideals of unregulated free markets. These political implications have created an environment of rhetorical adversity in which disinformation abounds, thus compounding the challenges for climate communicators. The existing literature on how to communicate climate change and dispel misinformation converges on several conclusions: First, providing information about climate change, in particular explanations of why it occurs, can enhance people's acceptance of science. Second, highlighting the scientific consensus can be an effective means to counter misinformation and raise public acceptance. Third, culturally aligned messages and messengers are more likely to be successful. Finally, climate misinformation is best defanged, through a process known as inoculation, before it is encountered, although debunking techniques can also be successful.

Evaluating Rumor Debunking Effectiveness During the COVID-19 Pandemic Crisis: Utilizing User Stance in Comments on Sina Weibo

Background: The spread of rumors related to COVID-19 on social media has posed substantial challenges to public health governance, and thus exposing rumors and curbing their spread quickly and effectively has become an urgent task. This study aimed to assist in formulating effective strategies to debunk rumors and curb their spread on social media.

Methods: A total of 2,053 original postings and 100,348 comments that replied to the postings of five false rumors related to COVID-19 (dated from January 20, 2020, to June 28, 2020) belonging to three categories, authoritative, social, and political, on Sina Weibo in China were randomly selected. To study the effectiveness of different debunking methods, a new annotation scheme was proposed that divides debunking methods into six categories: denial, further fact-checking, refutation, person response, organization response, and combination methods. Text classifiers using deep learning methods were built to automatically identify four user stances in comments that replied to debunking postings: supporting, denying, querying, and commenting stances. Then, based on stance responses, a debunking effectiveness index (DEI) was developed to measure the effectiveness of different debunking methods.

Results: The refutation method with cited evidence has the best debunking effect, whether used alone or in combination with other debunking methods. For the social category of Car rumor and political category of Russia rumor, using the refutation method alone can achieve the optimal debunking effect. For authoritative rumors, a combination method has the optimal debunking effect, but the most effective combination method requires avoiding the use of a combination of a debunking method where the person or organization defamed by the authoritative rumor responds personally and the refutation method.

Conclusion: The findings provide relevant insights into ways to debunk rumors effectively, support crisis management of false information, and take necessary actions in response to rumors amid public health emergencies.

A Revised Framework for the Investigation of Expectation Update Versus Maintenance in the Context of Expectation Violations: The ViolEx 2.0 Model

Expectations are probabilistic beliefs about the future that shape and influence our perception, affect, cognition, and behavior in many contexts. This makes expectations a highly relevant concept across basic and applied psychological disciplines. When expectations are confirmed or violated, individuals can respond by either updating or maintaining their prior expectations in light of the new evidence. Moreover, proactive and reactive behavior can change the probability with which individuals encounter expectation confirmations or violations. The investigation of predictors and mechanisms underlying expectation update and maintenance has been approached from many research perspectives. However, in many instances there has been little exchange between different research fields. To further advance research on expectations and expectation violations, collaborative efforts across different disciplines in psychology, cognitive (neuro)science, and other life sciences are warranted. For fostering and facilitating such efforts, we introduce the ViolEx 2.0 model, a revised framework for interdisciplinary research on cognitive and behavioral mechanisms of expectation update and maintenance in the context of expectation violations. To support different goals and stages in interdisciplinary exchange, the ViolEx 2.0 model features three model levels with varying degrees of specificity in order to address questions about the research synopsis, central concepts, or functional processes and relationships, respectively. The framework can be applied to different research fields and has high potential for guiding collaborative research efforts in expectation research.

The echo chamber effect of rumor rebuttal behavior of users in the early stage of COVID-19 epidemic in China

During public health emergencies, as one of the most effective rumor management strategies, rumor rebuttals depend on users' cognition, decision-making and interactive behaviors. Taking the dissemination of rumor rebuttals related to COVID-19 epidemic in the early stage in China as an example, we firstly adapted network analysis to construct representative networks of information and communication flow networks of users based on users' retweeting and commenting behaviors. Then quantitative indicators and exponential random graph models were used to evaluate the level of homophily based on topic and veracity in information networks, identity and standpoint in user networks. Meanwhile, chi square tests were added to compare the degree of echo chamber effect in retweeting and commenting. Findings showed that, users did show significant echo chamber effect when retweeting or commenting on rumor rebuttal information with different veracity. They showed diversification when retweeting but a certain tendency and pertinence when commenting in topic selection. Weibo's direct and open platform for retweeting and commenting broke the boundaries between stakeholders from different professional fields. However, the retweeting mechanism promoted self-isolation of users' standpoints, while the commenting mechanism provided an understanding and integrating channel for groups with opposing standpoints.

Characterizing partisan political narrative frameworks about COVID-19 on Twitter

The COVID-19 pandemic is a global crisis that has been testing every society and exposing the critical role of local politics in crisis response. In the United States, there has been a strong partisan divide between the Democratic and Republican party's narratives about the pandemic which resulted in polarization of individual behaviors and divergent policy adoption across regions. As shown in this case, as well as in most major social issues, strongly polarized narrative frameworks facilitate such narratives. To understand polarization and other social chasms, it is critical to dissect these diverging narratives. Here, taking the Democratic and Republican political social media posts about the pandemic as a case study, we demonstrate that a combination of computational methods can provide useful insights into the different contexts, framing, and characters and relationships that construct their narrative frameworks which individual posts source from. Leveraging a dataset of tweets from the politicians in the U.S., including the ex-president, members of Congress, and state governors, we found that the Democrats' narrative tends to be more concerned with the pandemic as well as financial and social support, while the Republicans discuss more about other political entities such as China. We then perform an automatic framing analysis to characterize the ways in which they frame their narratives, where we found that the Democrats emphasize the government's role in responding to the pandemic, and the Republicans emphasize the roles of individuals and support for small businesses. Finally, we present a semantic role analysis that uncovers the important characters and relationships in their narratives as well as how they facilitate a membership categorization process. Our findings concretely expose the gaps in the "elusive consensus" between the two parties. Our methodologies may be applied to computationally study narratives in various domains.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1140/epjds/s13688-021-00308-4.

LOCO: The 88-million-word language of conspiracy corpus

The spread of online conspiracy theories represents a serious threat to society. To understand the content of conspiracies, here we present the language of conspiracy (LOCO) corpus. LOCO is an 88-million-token corpus composed of topic-matched conspiracy (N = 23,937) and mainstream (N = 72,806) documents harvested from 150 websites. Mimicking internet user behavior, documents were identified using Google by crossing a set of seed phrases with a set of websites. LOCO is hierarchically structured, meaning that each document is cross-nested within websites (N = 150) and topics (N = 600, on three different resolutions). A rich set of linguistic features (N = 287) and metadata includes upload date, measures of social media engagement, measures of website popularity, size, and traffic, as well as political bias and factual reporting annotations. We explored LOCO's features from different perspectives showing that documents track important societal events through time (e.g., Princess Diana's death, Sandy Hook school shooting, coronavirus outbreaks), while patterns of lexical features (e.g., deception, power, dominance) overlap with those extracted from online social media communities dedicated to conspiracy theories. By computing within-subcorpus cosine similarity, we derived a subset of the most representative conspiracy documents (N = 4,227), which, compared to other conspiracy documents, display prototypical and exaggerated conspiratorial language and are more frequently shared on Facebook. We also show that conspiracy website users navigate to websites via more direct means than mainstream users, suggesting confirmation bias. LOCO and related datasets are freely available at https://osf.io/snpcg/ .

Negative Perception of the COVID-19 Pandemic Is Dropping: Evidence From Twitter Posts

The COVID-19 pandemic hit hard society, strongly affecting the emotions of the people and wellbeing. It is difficult to measure how the pandemic has affected the sentiment of the people, not to mention how people responded to the dramatic events that took place during the pandemic. This study contributes to this discussion by showing that the negative perception of the people of the COVID-19 pandemic is dropping. By negative perception, we mean the number of negative words the users of Twitter, a social media platform, employ in their online posts. Seen as aggregate, Twitter users are using less and less negative words as the pandemic evolves. The conclusion that the negative perception is dropping comes from a careful analysis we made in the contents of the COVID-19 Twitter chatter dataset, a comprehensive database accounting for more than 1 billion posts generated during the pandemic. We explore why the negativity of the people decreases, making connections with psychological traits such as psychophysical numbing, reappraisal, suppression, and resilience. In particular, we show that the negative perception decreased intensively when the vaccination campaign started in the USA, Canada, and the UK and has remained to decrease steadily since then. This finding led us to conclude that vaccination plays a key role in dropping the negativity of the people, thus promoting their psychological wellbeing.

Identifying False Human Papillomavirus (HPV) Vaccine Information and Corresponding Risk Perceptions From Twitter: Advanced Predictive Models

Background

The vaccination uptake rates of the human papillomavirus (HPV) vaccine remain low despite the fact that the effectiveness of HPV vaccines has been established for more than a decade. Vaccine hesitancy is in part due to false information about HPV vaccines on social media. Combating false HPV vaccine information is a reasonable step to addressing vaccine hesitancy.

Objective

Given the substantial harm of false HPV vaccine information, there is an urgent need to identify false social media messages before it goes viral. The goal of the study is to develop a systematic and generalizable approach to identifying false HPV vaccine information on social media.

Methods

This study used machine learning and natural language processing to develop a series of classification models and causality mining methods to identify and examine true and false HPV vaccine–related information on Twitter.

Results

We found that the convolutional neural network model outperformed all other models in identifying tweets containing false HPV vaccine–related information (F score=91.95). We also developed completely unsupervised causality mining models to identify HPV vaccine candidate effects for capturing risk perceptions of HPV vaccines. Furthermore, we found that false information contained mostly loss-framed messages focusing on the potential risk of vaccines covering a variety of topics using more diverse vocabulary, while true information contained both gain- and loss-framed messages focusing on the effectiveness of vaccines covering fewer topics using relatively limited vocabulary.

Conclusions

Our research demonstrated the feasibility and effectiveness of using predictive models to identify false HPV vaccine information and its risk perceptions on social media.

Trends of Multidrug-Resistant Pathogens, Difficult to Treat Bloodstream Infections, and Antimicrobial Consumption at a Tertiary Care Center in Lebanon from 2015-2020: COVID-19 Aftermath

Introduction: We studied the trend of antimicrobial resistance and consumption at Saint George Hospital University Medical Center (SGHUMC), a tertiary care center in Beirut, Lebanon, with a focus on the SARS-CoV-2 pandemic. Materials and Methods: We calculated the isolation density/1000 patient-days (PD) of the most isolated organisms from 1 January 2015–31 December 2020 that included: E. coli (Eco), K. pneumoniae (Kp), P. aeruginosa (Pae), A. baumannii (Ab), S. aureus (Sau), and E. faecium (Efm). We considered March–December 2020 a surrogate of COVID-19. We considered one culture/patient for each antimicrobial susceptibility and excluded Staphylococcus epidermidis, Staphylococcus coagulase-negative, and Corynebacterium species. We analyzed the trends of the overall isolates, the antimicrobial susceptibilities of blood isolates (BSI), difficult-to-treat (DTR) BSI, carbapenem-resistant Enterobacteriaceae (CRE) BSI, and restricted antimicrobial consumption as daily-defined-dose/1000 PD. DTR implies resistance to carbapenems, beta-lactams, fluoroquinolones, and additional antimicrobials where applicable. Results and Discussion: After applying exclusion criteria, we analyzed 1614 blood cultures out of 8314 cultures. We isolated 85 species, most commonly Eco, at 52%. The isolation density of total BSI in 2020 decreased by 16%: 82 patients were spared from bacteremia, with 13 being DTR. The isolation density of CRE BSI/1000 PD decreased by 64% from 2019 to 2020, while VREfm BSI decreased by 34%. There was a significant decrease of 80% in Ab isolates (p-value < 0.0001). During COVID-19, restricted antimicrobial consumption decreased to 175 DDD/1000 PD (p-value < 0.0001). Total carbapenem consumption persistently decreased by 71.2% from 108DDD/1000 PD in 2015–2019 to 31 DDD/1000 PD in 2020. At SGHUMC, existing epidemics were not worsened by the pandemic. We attribute this to our unique and dynamic collaboration of antimicrobial stewardship, infection prevention and control, and infectious disease consultation.

The COVID-19 pandemic and the search for structure: Social media and conspiracy theories

The study outlines a model for how the COVID-19 pandemic has uniquely exacerbated the propagation of conspiracy beliefs and subsequent harmful behaviors. The pandemic has led to widespread disruption of cognitive and social structures. As people face these disruptions they turn online seeking alternative cognitive and social structures. Once there, social media radicalizes beliefs, increasing contagion (rapid spread) and stickiness (resistance to change) of conspiracy theories. As conspiracy theories are reinforced in online communities, social norms develop, translating conspiracy beliefs into real-world action. These real-world exchanges are then posted back on social media, where they are further reinforced and amplified, and the cycle continues. In the broader population, this process draws attention to conspiracy theories and those who confidently espouse them. This attention can drive perceptions that conspiracy beliefs are less fringe and more popular, potentially normalizing such beliefs for the mainstream. We conclude by considering interventions and future research to address this seemingly intractable problem.