Health Misinformation Research Articles

Comparative analysis of health authorities’ spokespersons and health influencers during the COVID-19 pandemic

Background: Concerns over an infodemic following a surge in health misinformation circulating on social media set out the government's priority for Indonesia. Given the urgent work on the coronavirus disease 2019 (COVID-19) response, the government collaborated with health-related spokespersons and influencers with a medical background by starting a COVID-19 public education campaign on social media. A collaborative initiative involved health spokespersons from government and non-government to clarify misinformation about COVID-19. Methods: The primary purpose of this research is to compare government and non-government spokespersons by examining their roles in educating about COVID-19 vaccines and health services. This study employed comparative factor analysis and non-participatory observation toward the media activity of spokespersons in Indonesia. Using a questionnaire, this study examined the dimensions of public campaigns, risk communication, health and emergency, leadership and communication from Indonesian spokespersons. The data collection was conducted in two stages: a pilot study involving 102 respondents and the actual study involving 276 respondents. Results: Findings show that utilizing spokespersons is important due to its capabilities of reaching diverse audiences and improving public engagement, trustworthiness, and credibility. Conclusions: Combining health authorities’ spokespersons and health influencers in Indonesia, this study provides valuable insights for communication management in developing and supporting the role of health authorities from the government, non-government as well as medical sectors.

Alternative health groups on social media, misinformation, and the (de)stabilization of ontological security

Through 19 semistructured interviews, we explore why people join and participate in “alternative health” Facebook groups and Reddit subs. Participation in these groups creates an ontological circle where people’s feelings of fear, desperation, and distrust in the systems and actors that comprise our government, health, and news systems inspire alternative information and support-seeking in these groups and subs. While participation assuages those feelings and stabilizes people’s sense of ontological security, it also destabilizes it, reinforcing and legitimizing feelings of fear, desperation, and distrust. This circle contributes to favorable receptive conditions for spreading unproven and dangerous health misinformation. We argue that it will be difficult to address misinformation (1) without considering and rectifying people’s often valid reasons for feelings of fear, desperation, distrust, and their desires for information and (2) without considering the relationships between alternative health social media groups and subs and the (de)stabilization of ontological security.

Health Misinformation on Social Media and its Impact on COVID-19 Vaccine Inoculation in Jordan

It is vital to understand the nature of misinformation disseminated online regarding the COVID-19 vaccination. This understanding will enhance governments’ efforts and strategies to combat the factors which hinder vaccine uptake. Vaccine hesitancy has always been a challenge which has accompanied vaccine rollouts. Misinformation regarding the COVID-19 vaccination, along with the ambiguous narratives around the origin of the virus, has played a role in vaccine hesitancy among Jordanians. The online activity generated by social media during the pandemic, due to people’s fear of the virus, their general anxiety and curiosity, and their desire for updates, made social media an even more fertile environment for misinformation than ever before. During the COVID-19 pandemic in Jordan, misinformation on social media platforms amplified the scale of fears around the safety of the vaccination programme. Therefore, this study offers an exploration of, and insight into, the thoughts and experiences of a sample of 30 Jordanian citizens who are hesitant about COVID-19 vaccination. This study uses a qualitative approach in order to further understand vaccine hesitancy and the nature of misinformation surrounding it, using semi-structured, face-to-face interviews with participants. It found that low levels of information about health, misconceptions about the COVID-19 vaccine, and the spread of misinformation on social media were all causes of vaccine hesitancy in Jordan. Facebook and WhatsApp were the principal social media networks identified in this study as spreading misinformation about the vaccine. The study sample reported that they believed in the conspiracy theories discussed on these two platforms. Furthermore, videos of influencers and anti-vaccination medical doctors from overseas played a part in misleading individuals regarding inoculation against COVID-19. Additionally, other factors were also identified and are discussed in this study.

Detecting and classifying online health misinformation with 'Content Similarity Measure (CSM)' algorithm: an automated fact-checking-based approach.

Information dissemination occurs through the 'word of media' in the digital world. Fraudulent and deceitful content, such as misinformation, has detrimental effects on people. An implicit fact-based automated fact-checking technique comprising information retrieval, natural language processing, and machine learning techniques assist in assessing the credibility of content and detecting misinformation. Previous studies focused on linguistic and textual features and similarity measures-based approaches. However, these studies need to gain knowledge of facts, and similarity measures are less accurate when dealing with sparse or zero data. To fill these gaps, we propose a 'Content Similarity Measure (CSM)' algorithm that can perform automated fact-checking of URLs in the healthcare domain. Authors have introduced a novel set of content similarity, domain-specific, and sentiment polarity score features to achieve journalistic fact-checking. An extensive analysis of the proposed algorithm compared with standard similarity measures and machine learning classifiers showed that the 'content similarity score' feature outperformed other features with an accuracy of 88.26%. In the algorithmic approach, CSM showed improved accuracy of 91.06% compared to the Jaccard similarity measure with 74.26% accuracy. Another observation is that the algorithmic approach outperformed the feature-based method. To check the robustness of the algorithms, authors have tested the model on three state-of-the-art datasets, viz. CoAID, FakeHealth, and ReCOVery. With the algorithmic approach, CSM showed the highest accuracy of 87.30%, 89.30%, 85.26%, and 88.83% on CoAID, ReCOVery, FakeHealth (Story), and FakeHealth (Release) datasets, respectively. With a feature-based approach, the proposed CSM showed the highest accuracy of 85.93%, 87.97%, 83.92%, and 86.80%, respectively.

Why do people believe health misinformation? – MedFake study results

Why do people believe health misinformation? – MedFake study results

An Architecture Proposal for Noncommunicable Diseases Prevention

Noncommunicable Diseases (NCDs) are a leading global health challenge, causing 41 million deaths per year. Risk factors include genetics, environmental factors, and lifestyle choices. Adopting healthy lifestyles can prevent or delay the onset of NCDs, but health misinformation can lead people to make poor decisions about their health.To combat this, it is proposed to develop an Intelligent System using Artificial Intelligence techniques to collect and analyze data from social media about health topics to combat misinformation in public health and forecast NCDs, providing guidelines to prevent their spread. Methods: A system´s overall architecture is presented ... An innovative and novel solution that addresses the spread of information concerning health and NCDs contributes to inform public policies and infodemic management strategies

The Impact of the COVID-19 Pandemic on Uptake of Routine Childhood Immunization: How a Behavioral Science Led Analysis Can Strengthen Vaccination Policy

In addition to the direct health and economic impacts of the COVID-19 pandemic, routine immunization of children for vaccine-preventable diseases has also suffered. Quantitative studies have shown evidence of backsliding in routine immunization, i.e., children failing to receive their vaccinations on schedule. Pandemic restrictions were the cause for some of this behavior, and there are signs that subsequent campaigns and supply-side initiatives have influenced trends toward routine immunization catch-up. However, limited qualitative research indicates that the pandemic may have disrupted parents/caregivers’ decision-making processes around routine immunization, and their attitudes toward vaccination, in ways that could have long-term effects on rates of immunization. Further behavioral research can help elucidate this and provide guidance to policymakers to boost immunization uptake. This policy article explores the decision dynamics leading to missed routine immunization doses during the pandemic, through an analysis of secondary literature, a summary of primary-research studies on COVID-19 vaccine hesitancy conducted during the pandemic across Côte d’Ivoire, Burkina Faso, Kenya and Pakistan, and a summary of a study on the impact of COVID-19 on routine immunization uptake in South Africa. We found that the COVID-19 pandemic severely disrupted prior patterns of engagement with the health system, turning routine immunization from a default decision for parents/caregivers into an “active decision-making” moment. Restrictions on movement, limited routine immunization supplies and other practical impediments combined with fear of contracting COVID-19 at health facilities, misinformation, and fear of vaccine side-effects, creating an emergent behavior of missed immunization doses during the pandemic period. We also found that hesitancy about COVID-19 vaccines may be transferred onto previously accepted vaccines, impacting routine immunization. We identified three decision-making pathways for routine immunization uptake among South African parents/caregivers once the lockdowns were lifted: the “path of procrastination,” the “path of doubt,” and the “path of persistence.” We highlight the policy implications of these, and suggest potential interventions to increase routine immunization uptake for parents/caregivers on each path, as well as for general communications, service delivery, and pandemic readiness.

Legal Solutions to Health Misinformation and Disinformation During the Covid-19 Pandemic Outbreak

The spread of false information online has increased dramatically in recent years. Around the COVID-19 pandemic,a digital epidemic of misinformation, or “infodemic,” has arisen. Since the beginning of 2020, digital platformshave served as both a source of and a remedy for Disinformation. After a few months of the epidemic, it is clearthat ensuring health professionals access to necessary medical equipment and supplies is less crucial than fightingfalsehoods online. Actions such as (a) urging social media firms to delete material about the epidemic that iscontentious, (b) putting up special forces to counteract propaganda and false information (e.g., E.U., U.K.), and (c)incarcerating suspicious coronavirus false claims, including in connection to the public health interventions, werehigh priorities for many government agencies around the world in response to COVID-19. State of emergencymeasures used early on shaped future debates on how to strike a fair balance between free speech and censorshipon the internet by, among other things, restricting access to news and encouraging journalists to hold back on theirreporting.

Do elders care about eHealth? A correlational study between eHealth consumption and literacy

As digital tools become more prevalent in medicine, the ability for individuals to understand and take actions based on what they read on the internet is crucial. eHealth literacy is defined as as the ability to seek, find, understand, and evaluate health information from electronic sources and apply the knowledge gained to addressing or solving a health problem. In general, Americans have low eHealth literacy rates. However, limited research has been conducted to understand the eHealth literacy level among older Chinese adult immigrants in the U.S. To determine the eHealth literacy of elderly Chinese immigrants, we sent out an eHealth survey and relevant computer skills survey using a modified version of the eHEALS (eHealth Literacy Scale) health literacy test. We hypothesized that elders who consumed more electronic health content would have a higher eHealth literacy score. The results of this survey showed that there was a positive correlation between the frequency of electronic health information consumption and the participant's eHealth literacy rate. In addition, the results of our computer literacy test show that the frequency of consumption and computer literacy are positively correlated as well. There is a strong positive correlation between the level of computer skills and eHealth literacy of participants. These results reveal possible steps individuals can take to reduce health misinformation and improve their own health by attaining, understanding, and taking action on health material on the internet.

Exploring the impact of digital health literacy on COVID-19 behaviors in New York state college students during the COVID-19 pandemic

Early in 2020, the COVID-19 pandemic became a global public health concern. College students became dependent on the online environment for learning, but also to receive COVID-19 information. Understanding digital health literacy and subsequent prevention behaviors in a digitally connected population during a public health crisis is crucial to prepare for future pandemics. This study focused on college students in the United States and explored whether digital health literacy predicted their main source of pandemic information, adherence to public health guidelines, and intentions to receive a COVID-19 vaccine. During the summer of 2020, 254 New York State college students completed the survey. Digital health literacy was found to predict using ‘Government agencies websites’ as a main source of information and adherence to public health guidelines. It was not found to predict vaccine intentions. The findings confirm the importance of digital health literacy interventions in younger populations, especially with the rise of health misinformation available on the Internet.

Exploring online oral health misinformation: a content analysis.

Considering the unfavorable implications of health falsehoods and the lack of dental research into information disorder, this study aimed to identify and characterize online oral health misinformation. A total of 410 websites published in English were retrieved using Google Advanced Search and screened by two independent investigators to compile falsehoods through thematic content analysis. Afterward, 318 pieces of misinformation were consensually divided into four groups concerning their informational interest (G1), financial, psychological, and social interests produced/disseminated by non-dental professionals (G2) or by dental professionals (G3), and political interests (G4). Social media (Facebook and Instagram) and fact-checking tool (Snopes) were also screened to determine the spread of falsehoods by identifying corresponding posts and warnings. As a result, misinformation was mainly associated with gum diseases (12.0%), root canal treatment (11.6%), toothache (10.4%), fluoride (10.4%), and dental caries (9.8%), with a special highlight on recommendations for the usage of natural products, toxicity concerns, and anti-fluoridation propaganda. Additionally, most misinformation was allocated in G3 (41.9%), which presented a statistically higher frequency of financial interests than G4. Finally, falsehoods were considerably identified on Facebook (62.9%) and Instagram (49.4%), especially G3 and G4. Nevertheless, Snopes has debunked only 5.9% of these content items. Therefore, misinformation was predominantly produced or disseminated by dental professionals mainly motivated by financial interests and usually linked to alternative/natural treatments. Although these items were shared on social media, fact-checking agencies seemed to have limited knowledge about their dissemination.

Nurses facing health misinformation: How to spot scientific articles misuse?

Currently, large amounts of health information, mainly in the social media field, have led to an infodemic which, together with the vast misleading and inaccurate information that can be accessed, represents a substantial public health issue. Healthcare professionals can help to identify and even prevent the dissemination of such information, as well as to lead the struggle against it by denying it. Therefore, the aim of this paper is to propose a guide to be used for the detection of health misinformation focused on health professionals. The model is based on an in-depth analysis, focused on assessing the contextualization of the type of scientific document, the possibility of extrapolation of the information, the causality, as well as the quality of the scientific evidence given. Besides requesting an effort from healthcare professionals to prevent the spread of health disinformation, we believe it is essential to offer tools to easily detect it, whereby training in research methodology is the main tool for healthcare professionals in the fight against misinformation and its negative implications on people's health

The political economy of digital profiteering: communication resource mobilization by anti-vaccination actors.

Contemporary communication requires both a supply of content and a digital information infrastructure. Modern campaigns of misinformation are especially dependent on that back-end infrastructure for tracking and targeting a sympathetic audience and generating revenue that can sustain the campaign financially-if not enable profiteering. However, little is known about the political economy of misinformation, particularly those campaigns spreading misleading or harmful content about public health guidelines and vaccination programs. To understand the political economy of health misinformation, we analyze the content and infrastructure networks of 59 groups involved in communicating misinformation about vaccination programs. With a unique collection of tracker and communication infrastructure data, we demonstrate how the political economy of misinformation depends on platform monetization infrastructures. We offer a theory of communication resource mobilization that advances understanding of the communicative context, organizational interactions, and political outcomes of misinformation production.

“I Do Not Trust Health Information Shared by My Parents”: Credibility Judgement of Health (Mis)information on Social Media in China

ABSTRACT The surge of health misinformation on social media poses a threat to public health. This qualitative study reports how users process health misinformation from the dominant strong-tie social media, WeChat, in China. We conducted ten on-site focus groups involving 76 adult participants. Drawing on the apomediation theory and the dual processing model of credibility assessment, we found the heuristic approach to processing health information was the dominant route of engagement. We identified four categories of credibility assessment cues, including (1) expertise, authority, and commercial intent of original sources, (2) expertise of apomediaries (i.e. social media information sharers) and generational bias, (3) clickbait and sensational content versus objective scientific style, and (4) disconfirmation versus confirmation bias. We highlight that apomediaries are playing an increasingly important role in informing credibility judgment. Specifically, younger adults have formed a generational bias of deeming older apomediaries as cues of lower credibility.

Health Literacy and Critical Lecture as Key Elements to Detect and Reply to Nutrition Misinformation on Social Media: Analysis between Spanish Healthcare Professionals.

Health misinformation about nutrition and other health aspects on social media is a current public health concern. Healthcare professionals play an essential role in efforts to detect and correct it. The present study focuses on analyzing the use of competencies associated with training in methodology, health literacy, and critical lecture in order to detect sources of health misinformation that use scientific articles to support their false information. A qualitative study was conducted between 15 and 30 January 2022, wherein the participants were recruited from active users from a nutrition conversation on Twitter, diets, and cancer and defined themselves as healthcare professionals. This study demonstrates that health literacy and critical lecture competencies allow for the detection of more misinformation messages and are associated with a high rate of responses to users that spread the misinformation messages. Finally, this study proposes the necessity of developing actions to improve health literacy and critical lecture competencies between healthcare professionals. However, in order to achieve this, health authorities must develop strategies to psychologically support those healthcare professionals faced with bullying as a result of their activity on social media debunking health hoaxes.

Visualizing the Interpretation of a Criteria-Driven System That Automatically Evaluates the Quality of Health News: Exploratory Study of 2 Approaches.

Machine learning techniques have been shown to be efficient in identifying health misinformation, but the results may not be trusted unless they can be justified in a way that is understandable. This study aimed to provide a new criteria-based system to assess and justify health news quality. Using a subset of an existing set of criteria, this study compared the feasibility of 2 alternative methods for adding interpretability. Both methods used classification and highlighting to visualize sentence-level evidence. A total of 3 out of 10 well-established criteria were chosen for experimentation, namely whether the health news discussed the costs of the intervention (the cost criterion), explained or quantified the harms of the intervention (the harm criterion), and identified the conflicts of interest (the conflict criterion). The first step of the experiment was to automate the evaluation of the 3 criteria by developing a sentence-level classifier. We tested Logistic Regression, Naive Bayes, Support Vector Machine, and Random Forest algorithms. Next, we compared the 2 visualization approaches. For the first approach, we calculated word feature weights, which explained how classification models distill keywords that contribute to the prediction; then, using the local interpretable model-agnostic explanation framework, we selected keywords associated with the classified criterion at the document level; and finally, the system selected and highlighted sentences with keywords. For the second approach, we extracted sentences that provided evidence to support the evaluation result from 100 health news articles; based on these results, we trained a typology classification model at the sentence level; and then, the system highlighted a positive sentence instance for the result justification. The number of sentences to highlight was determined by a preset threshold empirically determined using the average accuracy. The automatic evaluation of health news on the cost, harm, and conflict criteria achieved average area under the curve scores of 0.88, 0.76, and 0.73, respectively, after 50 repetitions of 10-fold cross-validation. We found that both approaches could successfully visualize the interpretation of the system but that the performance of the 2 approaches varied by criterion and highlighting the accuracy decreased as the number of highlighted sentences increased. When the threshold accuracy was ≥75%, this resulted in a visualization with a variable length ranging from 1 to 6 sentences. We provided 2 approaches to interpret criteria-based health news evaluation models tested on 3 criteria. This method incorporated rule-based and statistical machine learning approaches. The results suggested that one might visually interpret an automatic criterion-based health news quality evaluation successfully using either approach; however, larger differences may arise when multiple quality-related criteria are considered. This study can increase public trust in computerized health information evaluation.

Intention of health experts to counter health misinformation in social media: Effects of perceived threat to online users, correction efficacy, and self-affirmation.

Our study analyzes the intention of Chinese health experts (health professionals and medical students) to correct health misinformation in social media. In an experimental 2 × 2 × 2 between-subjects factorial design (n = 415), we manipulated the experts' perception of the threat that health misinformation poses for online users, their self-efficacy with respect to correcting misinformation, and their self-affirmation. To select the potential influence factors, we draw on self-affirmation theory and the extended parallel process model. Results of our experiment revealed that correction intention increases if experts perceive the threat for online users as severe, believe that they are capable of countering the impact of misinformation, and have a high motivation to maintain a positive self-image of caring for others. We discuss the consequences of our findings for motivating experts to help reduce the adverse effects of health misinformation in social media.

Twitter spam and false accounts prevalence, detection, and characterization: A survey

The issue of quantifying and characterizing various forms of social media manipulation and abuse has been at the forefront of the computational social science research community for over a decade. In this paper, I provide a (non-comprehensive) survey of research efforts aimed at estimating the prevalence of spam and false accounts on Twitter, as well as characterizing their use, activity, and behavior. I propose a taxonomy of spam and false accounts, enumerating known techniques used to create and detect them. Then, I summarize studies estimating the prevalence of spam and false accounts on Twitter. Finally, I report on research that illustrates how spam and false accounts are used for scams and frauds, stock market manipulation, political disinformation and deception, conspiracy amplification, coordinated influence, public health misinformation campaigns, radical propaganda and recruitment, and more. I will conclude with a set of recommendations aimed at charting the path forward to combat these problems.

Comparing button-based chatbots with webpages for presenting fact-checking results: A case study of health information

The unprecedented proliferation of online health misinformation poses a potential threat to public health. In recent times, several fact-checking organizations have adopted chatbots to present fact-checking results. However, it is unclear whether chatbots are more appropriate than traditional fact-checking websites for presenting these rigorous corrective messages. To answer this question, we compared a button-based chatbot with a traditional webpage for presenting fact-checking results. As fact-checkers’ expertise cues could influence users’ perception of fact-checking, we also considered the effect of expertise cues in our study. We conducted a 2 (interaction type: webpage vs. chatbot) × 2 (expertise cue: non-highlighted vs. highlighted) between-subjects online experiment (N=308). The results show that the chatbot leads to higher perceived ease of use, which in turn increases the effectiveness of fact-checking. The highlighted expertise cue tends to decrease users’ intention to use, especially when they interact with the webpage. Finally, we discuss the feasibility of using chatbots to disseminate fact-checking content and several design implications for the creation of an effective tool to fact-check health information.

Factors Contributing to Youth’s Exposure to Mental Health Misinformation on TikTok During the Covid-19 Pandemic

There is an increasing number of youth having access to smartphones and technology, especially throughout the COVID-19 pandemic (McClain, 2022). Social media, such as TikTok, grew exponentially during the pandemic alongside increased postings of mental health content. As the majority of TikTok users are young people, this paper examines the question: “what factors have contributed most to the increase in the use of potentially misleading online mental health information among youth during the COVID-19 pandemic?”. Reviewing previous research, this paper mainly applies descriptive and evaluation analysis to study the factors that prompted youth to seek mental health information on TikTok. This paper argues that barriers to mental health care and isolation during the COVID-19 pandemic have led young people to turn to TikTok for mental health information, in turn leading to youth to be more exposed to misinformation. This paper calls for improvements in mental health care accessibility for youth and identifies the root causes of increased misinformation exposure on TikTok. The increased mental health discussions that took place through TikTok should continue but it is imperative to explore ways to prevent information from overflowing and being unreliable for the users.