Limited Role of Bots in Spreading Vaccine-Critical Information Among Active Twitter Users in the United States: 2017-2019

Posted in Error: Did the CDC's Retraction of Aerosol Guidance Undercut Its Public Reputation?

While there is ample research on the influence of retracted scientific publications on author reputation, less is known about how a health organization's retraction of scientific guidance can impact public perceptions of the organization. This study centers on the aerosol guidance retraction of the Centers for Disease Control and Prevention (CDC) in 2020. X/Twitter social media data were collected via ForSight from September 15 to October 8, 2020, with a machine learning algorithm specifically developed and used to detect sentiment toward the CDC. Regression analyses of the non-bot sample (N = 265,326) tested for differences in CDC sentiment across four stages: 1) baseline; 2) CDC guidance change; 3) CDC retraction of the prior guidance change; and 4) CDC reversion to a tempered form of the initial guidance change. The results show that sentiment toward the CDC increased from Time 1 to Time 2, then decreased for Time 3 with the "posted in error" retraction, but then increased for Time 4 back to a level similar to Time 2. That public perceptions of the CDC could improve after these changes in scientific guidance may be attributed to its self-report of the retraction and reporting that the retraction was a result of unintentional error. This study connects theories of reputation management and trust repair with the growing empirical research on retractions of published scientific research to provide a theoretical explanation for how a major public health organization can mitigate damage to its reputation in the short term.

Discovering why people believe disinformation about healthcare

Disinformation-false information intended to cause harm or for profit-is pervasive. While disinformation exists in several domains, one area with great potential for personal harm from disinformation is healthcare. The amount of disinformation about health issues on social media has grown dramatically over the past several years, particularly in response to the COVID-19 pandemic. The study described in this paper sought to determine the characteristics of multimedia social network posts that lead them to believe and potentially act on healthcare disinformation. The study was conducted in a neuroscience laboratory in early 2022. Twenty-six study participants each viewed a series of 20 either honest or dishonest social media posts, dealing with various aspects of healthcare. They were asked to determine if the posts were true or false and then to provide the reasoning behind their choices. Participant gaze was captured through eye tracking technology and investigated through "area of interest" analysis. This approach has the potential to discover the elements of disinformation that help convince the viewer a given post is true. Participants detected the true nature of the posts they were exposed to 69% of the time. Overall, the source of the post, whether its claims seemed reasonable, and the look and feel of the post were the most important reasons they cited for determining whether it was true or false. Based on the eye tracking data collected, the factors most associated with successfully detecting disinformation were the total number of fixations on key words and the total number of revisits to source information. The findings suggest the outlines of generalizations about why people believe online disinformation, suggesting a basis for the development of mid-range theory.

Patterns of human and bots behaviour on Twitter conversations about sustainability

Sustainability is an issue of worldwide concern. Twitter is one of the most popular social networks, which makes it particularly interesting for exploring opinions and characteristics related to issues of social preoccupation. This paper aims to gain a better understanding of the activity related to sustainability that takes place on twitter. In addition to building a mathematical model to identify account typologies (bot and human users), different behavioural patterns were detected using clustering analysis mainly in the mechanisms of posting tweets and retweets). The model took as explanatory variables, certain characteristics of the user's profile and her/his activity. A lexicon-based sentiment analysis in the period from 2006 to 2022 was also carried out in conjunction with a keyword study based on centrality metrics. We found that, in both bot and human users, messages showed mostly a positive sentiment. Bots had a higher percentage of neutral messages than human users. With respect to the used keywords certain commonalities but also slight differences between humans and bots were identified.

Measuring the burden of infodemics with a research toolkit for connecting information exposure, trust, and health behaviours

BACKGROUND

During a public health emergency, accurate and useful information can be drowned out by questions, concerns, information voids, conflicting information, and misinformation. Very few studies connect information exposure and trust to health behaviours, which limits available evidence to inform when and where to act to mitigate the burden of infodemics, especially in low resource settings. This research describes the features of a toolkit that can support studies linking information exposure to health behaviours at the individual level.

METHODS

To meet the needs of the research community, we determined the functional and non-functional requirements of a research toolkit that can be used in studies measuring topic-specific information exposure and health behaviours. Most data-driven infodemiology research is designed to characterise content rather than measure associations between information exposure and health behaviours. Studies also tend to be limited to specific social media platforms, are unable to capture the breadth of individual information exposure that occur online and offline, and cannot measure differences in trust by information source or content. Studies are also designed very differently, limiting synthesis of results.

RESULTS

We demonstrate a way to address these requirements via a web-based study platform that includes an app that participants use to record topic-specific information exposure, a browser plugin for tracking access to relevant webpages, questionnaires that can be delivered at any time during a study, and app-based incentives for participation such as visual analytics to compare trust levels with other participants. Other features of the platform include the ability to tailor studies to local contexts, ease of use for participants, and frictionless sharing of de-identified data for aggregating individual participant data in international meta-analyses.

CONCLUSIONS

Our proposed solution will be able to capture detailed data about information exposure and health behaviour data, standardise study design while simultaneously supporting localisation, and make it easy to synthesise individual participant data across studies. Future research will need to evaluate the toolkit in realistic scenarios to understand the usability of the toolkit for both participants and investigators.

Investigating #vapingcessation in Twitter

Evidence suggests that an increasing number of e-cigarette users report intentions and attempts to quit vaping. Since exposure to e-cigarette-related content on social media may influence e-cigarette and other tobacco product use, including potentially e-cigarette cessation, we aimed to explore vaping cessation-related posts on Twitter by utilizing a mixed-methods approach. We collected tweets pertaining to vaping cessation for the time period between January 2022 and December 2022 using snscrape. Tweets were scraped for the following hashtags: #vapingcessation, #quitvaping, and #stopJuuling. Data were analysed using Azure Machine Learning and Nvivo 12 software. Sentiment analysis revealed that vaping cessation-related tweets typically embody positive sentiment and are mostly produced in the U.S. and Australia. Our qualitative analysis identified six emerging themes: vaping cessation support, promotion of vaping cessation, barriers and benefits to vaping cessation, personal vaping cessation, and usefulness of peer support for vaping cessation. Our findings imply that improved dissemination of evidence-based vaping cessation strategies to a broad audience through Twitter may promote vaping cessation at the population level.

Measuring the Burden of Infodemics: Summary of the Methods and Results of the Fifth WHO Infodemic Management Conference

Background

An infodemic is excess information, including false or misleading information, that spreads in digital and physical environments during a public health emergency. The COVID-19 pandemic has been accompanied by an unprecedented global infodemic that has led to confusion about the benefits of medical and public health interventions, with substantial impact on risk-taking and health-seeking behaviors, eroding trust in health authorities and compromising the effectiveness of public health responses and policies. Standardized measures are needed to quantify the harmful impacts of the infodemic in a systematic and methodologically robust manner, as well as harmonizing highly divergent approaches currently explored for this purpose. This can serve as a foundation for a systematic, evidence-based approach to monitoring, identifying, and mitigating future infodemic harms in emergency preparedness and prevention.

Objective

In this paper, we summarize the Fifth World Health Organization (WHO) Infodemic Management Conference structure, proceedings, outcomes, and proposed actions seeking to identify the interdisciplinary approaches and frameworks needed to enable the measurement of the burden of infodemics.

Methods

An iterative human-centered design (HCD) approach and concept mapping were used to facilitate focused discussions and allow for the generation of actionable outcomes and recommendations. The discussions included 86 participants representing diverse scientific disciplines and health authorities from 28 countries across all WHO regions, along with observers from civil society and global public health-implementing partners. A thematic map capturing the concepts matching the key contributing factors to the public health burden of infodemics was used throughout the conference to frame and contextualize discussions. Five key areas for immediate action were identified.

Results

The 5 key areas for the development of metrics to assess the burden of infodemics and associated interventions included (1) developing standardized definitions and ensuring the adoption thereof; (2) improving the map of concepts influencing the burden of infodemics; (3) conducting a review of evidence, tools, and data sources; (4) setting up a technical working group; and (5) addressing immediate priorities for postpandemic recovery and resilience building. The summary report consolidated group input toward a common vocabulary with standardized terms, concepts, study designs, measures, and tools to estimate the burden of infodemics and the effectiveness of infodemic management interventions.

Conclusions

Standardizing measurement is the basis for documenting the burden of infodemics on health systems and population health during emergencies. Investment is needed into the development of practical, affordable, evidence-based, and systematic methods that are legally and ethically balanced for monitoring infodemics; generating diagnostics, infodemic insights, and recommendations; and developing interventions, action-oriented guidance, policies, support options, mechanisms, and tools for infodemic managers and emergency program managers.

Utilizing community level factors to improve prediction of out of hospital cardiac arrest outcome using machine learning

OBJECTIVES

To evaluate the impact of community level information on the predictability of out-of-hospital cardiac arrest (OHCA) survival.

METHODS

We used the Cardiac Arrest Registry to Enhance Survival (CARES) to geocode 9,595 Chicago incidents from 2014 to 2019 into community areas. Community variables including crime, healthcare, and economic factors from public data were merged with CARES. The merged data were used to develop ML models for OHCA survival. Models were evaluated using Area Under the Receiver Operating Characteristic curve (AUROC) and features were analyzed using SHapley Additive exPansion (SHAP) values.

RESULTS

Baseline results using CARES data achieved an AUROC of 84%. The final model utilizing community variables increased the AUROC to 88%. A SHAP analysis between high and low performing community area clusters showed the high performing cluster is positively impacted by good health related features and good community safety features positively impact the low performing cluster.

CONCLUSION

Utilizing community variables helps predict neurologic outcomes with better performance than only CARES data. Future studies will use this model to perform simulations to identify interventions to improve OHCA survival.

Bots' Activity on COVID-19 Pro and Anti-Vaccination Networks: Analysis of Spanish-Written Messages on Twitter

This study aims to analyze the role of bots in the dissemination of health information, both in favor of and opposing vaccination against COVID-19. Study design: An observational, retrospective, time-limited study was proposed, in which activity on the social network Twitter was analyzed. Methods: Data related to pro-vaccination and anti-vaccination networks were compiled from 24 December 2020 to 30 April 2021 and analyzed using the software NodeXL and Botometer. The analyzed tweets were written in Spanish, including keywords that allow identifying the message and focusing on bots’ activity and their influence on both networks. Results: In the pro-vaccination network, 404 bots were found (14.31% of the total number of users), located mainly in Chile (37.87%) and Spain (14.36%). The anti-vaccination network bots represented 16.19% of the total users and were mainly located in Spain (8.09%) and Argentina (6.25%). The pro-vaccination bots generated greater impact than bots in the anti-vaccination network (p < 0.000). With respect to the bots’ influence, the pro-vaccination network did have a significant influence compared to the activity of human users (p < 0.000). Conclusions: This study provides information on bots’ activity in pro- and anti-vaccination networks in Spanish, within the context of the COVID-19 pandemic on Twitter. It is found that bots in the pro-vaccination network influence the dissemination of the pro-vaccination message, as opposed to those in the anti-vaccination network. We consider that this information could provide guidance on how to enhance the dissemination of public health campaigns, but also to combat the spread of health misinformation on social media.

Media Data and Vaccine Hesitancy: Scoping Review

Background

Media studies are important for vaccine hesitancy research, as they analyze how the media shapes risk perceptions and vaccine uptake. Despite the growth in studies in this field owing to advances in computing and language processing and an expanding social media landscape, no study has consolidated the methodological approaches used to study vaccine hesitancy. Synthesizing this information can better structure and set a precedent for this growing subfield of digital epidemiology.

Objective

This review aimed to identify and illustrate the media platforms and methods used to study vaccine hesitancy and how they build or contribute to the study of the media's influence on vaccine hesitancy and public health.

Methods

This study followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A search was conducted on PubMed and Scopus for any studies that used media data (social media or traditional media), had an outcome related to vaccine sentiment (opinion, uptake, hesitancy, acceptance, or stance), were written in English, and were published after 2010. Studies were screened by only 1 reviewer and extracted for media platform, analysis method, the theoretical models used, and outcomes.

Results

In total, 125 studies were included, of which 71 (56.8%) used traditional research methods and 54 (43.2%) used computational methods. Of the traditional methods, most used content analysis (43/71, 61%) and sentiment analysis (21/71, 30%) to analyze the texts. The most common platforms were newspapers, print media, and web-based news. The computational methods mostly used sentiment analysis (31/54, 57%), topic modeling (18/54, 33%), and network analysis (17/54, 31%). Fewer studies used projections (2/54, 4%) and feature extraction (1/54, 2%). The most common platforms were Twitter and Facebook. Theoretically, most studies were weak. The following five major categories of studies arose: antivaccination themes centered on the distrust of institutions, civil liberties, misinformation, conspiracy theories, and vaccine-specific concerns; provaccination themes centered on ensuring vaccine safety using scientific literature; framing being important and health professionals and personal stories having the largest impact on shaping vaccine opinion; the coverage of vaccination-related data mostly identifying negative vaccine content and revealing deeply fractured vaccine communities and echo chambers; and the public reacting to and focusing on certain signals-in particular cases, deaths, and scandals-which suggests a more volatile period for the spread of information.

Conclusions

The heterogeneity in the use of media to study vaccines can be better consolidated through theoretical grounding. Areas of suggested research include understanding how trust in institutions is associated with vaccine uptake, how misinformation and information signaling influence vaccine uptake, and the evaluation of government communications on vaccine rollouts and vaccine-related events. The review ends with a statement that media data analyses, though groundbreaking in approach, should supplement-not supplant-current practices in public health research.

Exploring Coronavirus Disease 2019 Vaccine Hesitancy on Twitter Using Sentiment Analysis and Natural Language Processing Algorithms

BACKGROUND

Vaccination can help control the coronavirus disease 2019 (COVID-19) pandemic but is undermined by vaccine hesitancy. Social media disseminates information and misinformation regarding vaccination. Tracking and analyzing social media vaccine sentiment could better prepare health professionals for vaccination conversations and campaigns.

METHODS

A real-time big data analytics framework was developed using natural language processing sentiment analysis, a form of artificial intelligence. The framework ingests, processes, and analyzes tweets for sentiment and content themes, such as natural health or personal freedom, in real time. A later dataset evaluated the relationship between Twitter sentiment scores and vaccination rates in the United States.

RESULTS

The real-time analytics framework showed a widening gap in sentiment with more negative sentiment after vaccine rollout. After rollout, using a static dataset, an increase in positive sentiment was followed by an increase in vaccination. Lag cross-correlation analysis across US regions showed evidence that once all adults were eligible for vaccination, the sentiment score consistently correlated with vaccination rate with a lag of around 1 week. The Granger causality test further demonstrated that tweet sentiment scores may help predict vaccination rates.

CONCLUSIONS

Social media has influenced the COVID-19 response through valuable information and misinformation and distrust. This tool was used to collect and analyze tweets at scale in real time to study sentiment and key terms of interest. Separate tweet analysis showed that vaccination rates tracked regionally with Twitter vaccine sentiment and might forecast changes in vaccine uptake and/or guide targeted social media and vaccination strategies. Further work is needed to analyze the interplay between specific populations, vaccine sentiment, and vaccination rates.

Disinformation in Social Networks and Bots: Simulated Scenarios of Its Spread from System Dynamics

Social networks have become the scenario with the greatest potential for the circulation of disinformation, hence there is a growing interest in understanding how this type of information is spread, especially in relation to the mechanisms used by disinformation agents such as bots and trolls, among others. In this scenario, the potential of bots to facilitate the spread of disinformation is recognised, however, the analysis of how they do this is still in its initial stages. Taking into consideration what was previously stated, this paper aimed to model and simulate scenarios of disinformation propagation in social networks caused by bots based on the dynamics of this mechanism documented in the literature. For achieving the purpose, System dynamics was used as the main modelling technique. The results present a mathematical model, as far as disinformation by this mechanism is concerned, and the simulations carried out against the increase in the rate of activation and deactivation of bots. Thus, the preponderant role of social networks in controlling disinformation through this mechanism, and the potential of bots to affect citizens, is recognised.

#CoronaVirus and public health: the role of social media in sharing health information

Purpose

The purpose of this study is to examine how factual information and misinformation are being shared on Twitter by identifying types of social media users who initiate the information diffusion process.

Design/methodology/approach

This study used a mixed methodology approach to analyze tweets with COVID-19-related hashtags. First, a social network analysis was conducted to identify social media users who initiate the information diffusion process, followed by a quantitative content analysis of tweets by users with more than 5K retweets to identify what COVID-19 claims, factual information, misinformation and disinformation was shared on Twitter.

Findings

Results found very little misinformation and disinformation distributed widely. While health experts and journalists shared factual COVID-19-related information, they were not receiving optimum engagement. Tweets by citizens focusing on personal experience or opinions received more retweets and likes compared to any other sender type. Similarly, celebrities received more replies than any other sender type.

Practical implications

This study helps medical experts and government agencies understand the type of COVID-19 content and communication being shared on social media for population health purposes.

Originality/value

This study offers insight into how social media users engage with COVID-19-related information on Twitter and offers a typology of categories of information shared about the pandemic.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-03-2021-0143/.

Facilitators and Barriers of COVID-19 Vaccine Promotion on Social Media in the United States: A Systematic Review

Background and Objectives: Information regarding the COVID-19 pandemic has spread internationally through a variety of platforms, including social media. While efforts have been made to help reduce the spread of misinformation on social media, many platforms are still largely unregulated. The influence of social media use on vaccination promotion is not fully understood. This systematic review aims to identify facilitators and barriers associated with vaccine promotion through social media use. Materials and Methods: Reviewers analyzed 25 articles and identified common themes. Facilitators of vaccine promotion included an increase in the efforts of social media companies to reduce misinformation, the use of social media to spread information on public health and vaccine promotion, and the positive influence towards vaccinations of family and friends. Results and Conclusions: Identified barriers to vaccine promotion included the spread of misinformation, decreased vaccine acceptance among users of social media for COVID-19 related information due to polarization, and a lack of regulation on social media platforms. The results of this review provide insight for improving public health campaign promotion on social media and can help inform policy on social media regulation and misinformation prevention.

Characterizing the role of bots’ in polarized stance on social media

There is a rising concern with social bots that imitate humans and manipulate opinions on social media. Current studies on assessing the overall effect of bots on social media users mainly focus on evaluating the diffusion of discussions on social networks by bots. Yet, these studies do not confirm the relationship between bots and users’ stances. This study fills in the gap by analyzing if these bots are part of the signals that formulated social media users’ stances towards controversial topics. We analyze users’ online interactions that are predictive to their stances and identify the bots within these interactions. We applied our analysis on a dataset of more than 4000 Twitter users who expressed a stance on seven different topics. We analyzed those users’ direct interactions and indirect exposures with more than 19 million accounts. We identify the bot accounts for supporting/against stances, and compare them to other types of accounts, such as the accounts of influential and famous users. Our analysis showed that bot interactions with users who had specific stances were minimal when compared to the influential accounts. Nevertheless, we found that the presence of bots was still connected to users’ stances, especially in an indirect manner, as users are exposed to the content of the bots they follow, rather than by directly interacting with them by retweeting, mentioning, or replying.

Vaccine misinformation types and properties in Russian troll tweets

OBJECTIVE

To identify the content of and engagement with vaccine misinformation from Russian trolls on Twitter.

METHODS

Troll tweets (N = 1959) obtained from Twitter in 2020 were coded for vaccine misinformation (α = 0.77-0.97). Descriptive, bivariate, and multivariable negative binomial regressions were applied to estimate robust incidence rate ratios (IRRs) and 95% confidence intervals (95 %CI) of vaccine misinformation associations with tweet characteristics and engagement (i.e., replies, likes, retweets).

RESULTS

Misinformation about personal dangers (43.0%), civil liberty violations (20.2%), and vaccine conspiracies (18.6%) were common. More misinformation tweets used anti-vaccination language (97.3% vs. 13.2%) and referenced symptoms (37.4% vs. 0.5%) than non-misinformation tweets. Fewer misinformation tweets referenced credible sources (14.0% vs. 19.5%), were formatted as headlines (39.2% vs. 77.0%), and mentioned specific vaccines (11.3% vs. 36.1%, all p < 0.01) than non-misinformation tweets. Personal dangers misinformation had 83% lower rate of retweets (95 %CI 0.04-0.66). Civil liberties misinformation had significantly higher rate of replies (IRR: 7.65, 95 %CI 1.06-55.46), but lower overall engagement (IRR: 0.38, 95 %CI 0.16-0.88) than non-misinformation tweets.

CONCLUSIONS

Strategies used to promote vaccine misinformation provide insight into the nature of vaccine misinformation online and public responses. Our findings suggest a need to explore influences on whether users reject or entertain online vaccine misinformation.

Visual analysis of global research trends in social bots based on bibliometrics

Purpose

In order to further advance the research of social bots, based on the latest research trends and in line with international research frontiers, it is necessary to understand the global research situation in social bots.

Design/methodology/approach

Choosing Web of Science™ Core Collections as the data sources for searching social bots research literature, this paper visually analyzes the processed items and explores the overall research progress and trends of social bots from multiple perspectives of the characteristics of publication output, major academic communities and active research topics of social bots by the method of bibliometrics.

Findings

The findings offer insights into research trends pertaining to social bots and some of the gaps are also identified. It is recommended to further expand the research objects of social bots in the future, not only focus on Twitter platform and strengthen the research of social bot real-time detection methods and the discussion of the legal and ethical issues of social bots.

Originality/value

Most of the existing reviews are all for the detection methods and techniques of social bots. Unlike the above reviews, this study is a systematic literature review, through the method of quantitative analysis, comprehensively sort out the research output in social bots and shows the latest research trends in this area and suggests some research indirections that need to be focused in the future. The findings will provide references for subsequent scholars to research on social bots.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-06-2021-0336.

COVID-19 vaccine uptake and hesitancy survey in Northern Ireland and Republic of Ireland: Applying the theory of planned behaviour

The Coronavirus Disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) first appeared in Wuhan, China in late 2019 and since then has caused unprecedented economic and social disruption as well as presenting a major challenge to public health. Despite mass progress in COVID-19 vaccination uptake, vaccine hesitancy or anti-vax information has been reported that can delay public acceptance of a vaccine. An online cross-sectional survey (n = 439) assessed COVID-19 vaccine uptake and hesitancy in adults in Northern Ireland and the Republic of Ireland. Participants completed an adapted version of the Theory of Planned Behaviour Vaccine Questionnaire, the Vaccine Attitudes Scale (VAX), Vaccine Confidence Scale, and questions on previous experience of COVID-19. Results showed that 66.7% of the sample intended to get a vaccination as soon as possible, 27.15% reported they will get a vaccine when others get theirs and when it is clear there are no side effects. 6.15% had no intention of getting a vaccine. Overall, there is a high mean intention (M = 6.12) and confidence to get a COVID-19 vaccine. There was low vaccine hesitancy (M = 2.49) as measured by the VAX scale. A further analysis of the sub factors of the VAX showed there is uncertainty and mistrust of side effects for children. The finding demonstrate that the Theory of Planned Behaviour can be useful in making recommendations for public health considerations when encouraging vaccine uptake and reducing vaccine hesitancy.

Parental vaccine hesitancy: scope, causes, and potential responses

PURPOSE OF REVIEW

We reviewed the literature about parental vaccine hesitancy, focusing on publications from October 2019 to April 2021 to describe patterns and causes of hesitancy and interventions to address hesitancy.

RECENT FINDINGS

Recent studies expand understanding of the prevalence of vaccine hesitancy globally and highlight associated individual and contextual factors. Common concerns underlying hesitancy include uncertainty about the need for vaccination and questions about vaccine safety and efficacy. Sociodemographic factors associated with parental vaccine hesitancy vary across locations and contexts. Studies about psychology of hesitancy and how parents respond to interventions highlight the role of cognitive biases, personal values, and vaccination as a social contract or norm. Evidence-based strategies to address vaccine hesitancy include presumptive or announcement approaches to vaccine recommendations, motivational interviewing, and use of immunization delivery strategies like standing orders and reminder/recall programs. A smaller number of studies support use of social media and digital applications to improve vaccination intent. Strengthening school vaccine mandates can improve vaccination rates, but policy decisions must consider local context.

SUMMARY

Vaccine hesitancy remains a challenge for child health. Future work must include more interventional studies to address hesitancy and regular global surveillance of parental vaccine hesitancy and vaccine content on social media.

Healthcare professionals' acts of correcting health misinformation on social media

BACKGROUND

Health misinformation on social media is a public health concern, and healthcare professionals can help correct it. However, research on how they correct health misinformation on social media is rare.

OBJECTIVE

To develop a conceptual model that demonstrates how healthcare professionals correct health misinformation on social media.

METHODS

In-depth semi-structured interviews were conducted between January and March 2020 with 30 U.S. healthcare professionals (15 registered nurses and 15 medical doctors). Participants were recruited through purposive and snowball sampling and interviewed via mobile phone calls (using their mobile phone number) or apps (via Zoom or Skype). Interview data were analyzed using a grounded theory approach.

RESULTS

This study presents a two-phased conceptual model that shows healthcare professionals' acts of correcting health misinformation on social media (e.g., Twitter and Facebook). The first phase involves acts of authentication by which healthcare professionals verify health-related social media posts to be true or not. They undergo the process of internal acts of authentication (i.e., checking the author, checking for cues, checking the topic) and, if needed, external acts of authentication (i.e., examining the author and examining the content). When social media posts are deemed to contain health misinformation, they proceed to the second phase - acts of correction. In this phase, they undergo correction preparation (i.e., reflect, reveal, relate, and respect) and correction dissemination (i.e., private priming, public priming, public rebuttal, and private rebuttal).

CONCLUSIONS

The study proposed a conceptual model that shows how healthcare professionals correct health misinformation on social media. The findings can guide healthcare professionals when identifying and correcting health misinformation on and off social media, and can guide health authorities when developing campaigns against health misinformation.