Background:This study leveraged data mining from the question-and-answer social media platform Quora to explore attitudes and experiences concerning cannabis use and pregnancy. Objective:The primary aim was to identify and characterize online discourse specific to risk perceptions associated with cannabis use and pregnancy. Methods:Data collection encompassed two layers of Quora data: user-generated questions and answers to these questions. Keywords related to cannabis and pregnancy guided data selection and collection. Manual content coding was used for text analysis, with coding conducted by multiple coders, and interrater reliability assessed. Publicly available user profile metadata was reviewed to understand the roles of contributors participating in online discussions. A chi-square test was used to examine potential associations between sentiment toward cannabis use during pregnancy and sentiment toward general cannabis use. Results:We collected 7834 questions pertaining to cannabis use, with 6.8% (n=529) identified as relevant to topics about pregnancy. Questions spanned from January 2012-July 2023. Content analysis classified the detected themes into two stages of discussion: (1) cannabis use before pregnancy and (2) cannabis use during pregnancy, and seven subthemes: (a) safety of cannabis use (608/1074, 56.6%), (b) removal of cannabis from the body (372/1074, 34.6%), (c) legal consequences of cannabis use (175/1074, 16.3%), (d) cannabis use history before the pregnancy (27/529, 5.1%), (e) purpose of cannabis use (22/529, 4.2%), (f) public opinion comments (21/529, 4.0%), and (g) dosing recommendations (2/529, 0.4%). Among 1916 answers to these questions, 56.1% (n=1074) were relevant to cannabis and pregnancy topics. Among all answer contributors, 78.0% (660/846) were nonexpert observers (ie, users who did not publicly disclose professional credentials or affiliations on their Quora profiles), 6.3% (53/846) were people who had used cannabis during pregnancy, 11.3% (96/846) were expert observers (ie, health care providers, researchers), and 4.4% (37/846) were cannabis industry observers. Questions related to removing cannabis from the body attracted the highest engagement. Notably, analysis of answers found predominantly negative sentiment among Quora users regarding cannabis use during pregnancy. Conclusions:This study identified an active online community discussing cannabis and pregnancy on Quora. Results highlight a diversity of discussion topics, high frequency of nonexpert observers providing advice, and generally an overall negative sentiment toward cannabis use during pregnancy but not necessarily toward general cannabis use not associated with pregnancy. Additional research is needed to understand information gaps and misinformation in cannabis and pregnancy safety discussions online and how to conduct effective outreach to pregnant women and their families.

OBJECTIVE:This study aimed to describe the experience of nulliparous individuals aged <30 years when seeking permanent female contraception. STUDY DESIGN:We recruited nulliparous individuals aged <30 years who underwent permanent female contraception in the United States in the last 2 years through a clinical Listserv, Facebook, and Reddit to participate in semistructured in-depth interviews. We summarized themes using the socioecological model. RESULTS:Thirty individuals from 20 states participated. The average age was 25 years (range 21-30). Most participants identified as White (25, 83%), non-Latine (28, 93%), had attended some college (27, 90%), and used online resources to seek permanent female contraception (22, 73%). On an individual level, childfree identity and the experience of trying other contraception methods led participants to choose permanent female contraception. On an interpersonal level, participants reported others' support of bodily autonomy and childfree identity as facilitators and questioning permanent female contraception decisions as a barrier. On an institutional level, insurance coverage created confusion and stress. At the community level, social media interactions provided information and support. At the public policy level, the Dobbs decision increased the urgency to seek permanent female contraception. CONCLUSIONS:This qualitative study of young, nulliparous individuals focuses on characterizing patients' lived experiences and motivations for seeking permanent female contraception. Key findings included support stemming from a clinician's acceptance of childfree identity, stressors from insurance coverage, benefits of using online resources, and the impact of the Dobbs decision on the urgency to seek permanent female contraception due to perceived threats to reproductive autonomy. IMPLICATIONS:This study provides insights into the perspectives of young, nulliparous people who underwent permanent female contraception. Clinicians' acceptance of childfree-identity and online resources are facilitators, and the Dobbs decision impacted urgency of seeking surgery. Findings should be integrated into policy and practice for improved person-centered care in a post-Dobbs society.

Background: Though vaccine hesitancy and misinformation has been pervasive online, via platforms such as Twitter, little is known about the characteristics of pediatric-specific vaccine hesitancy and how online users interact with verified user accounts that may hold larger influence. Identifying specific COVID-19 pediatric vaccine hesitancy themes and online user interaction and sentiment may help inform health promotion that addresses vaccine hesitancy more effectively among parents and caregivers of pediatric populations. Methods: Keywords were used to query the public streaming twitter application programming interface to collect tweets associated with COVID-19 pediatric vaccines. From this corpus of tweets, we used topic modeling to output 20 topic clusters of tweet content and examined the 10 most retweeted tweets from each cluster to classify for relevance to pediatric COVID-19 vaccine hesitancy topics. Tweets were inductively coded to identify specific themes. Publicly available user metadata were assessed to identify verified accounts and self-reporting of racial or ethnic identity, and parental status. Replies to tweets were coded for user sentiment. A chi-squared test was used to determine the proportion of users agreeing with misinformation tweets Results: 863,007 tweets were collected between October 2020-October 2021. The 230 top tweets reviewed after outputting topic clusters accounted for 236,121 tweets and retweets. 84 unique tweets were identified as related to pediatric COVID-19 vaccine topics by verified users. Twenty three tweets (generating 44,509 retweets) contained misinformation-related themes. Seventy-one percent (n = 742) of user replies agreed with misinformation sentiment of the parent tweet. Main themes identified included vaccine development conspiracy, vaccine is experimental, and vaccine as a control tactic discussions. This study found that users who interacted with misinformation posted by verified accounts were more likely to agree than disagree with misinformation sentiment.

BACKGROUND AND AIMS:Cannabis-derived products (CDPs), including cannabidiol (CBD) and tetrahydrocannabinol (THC) products, are widely diverse and readily available through physical and online retail channels in the United States (US) marketplace and may also include claims of treating or providing benefit for health issues. This study aimed to systematically classify the various types of health benefit claim(s) present on CDP listings based on publicly available online marketplace data. DESIGN:Exploratory analysis to identify health benefit claims. SETTING AND CASES:A total of 624 805 unique CDPs sold in the US on Leafly and Weedmaps, cannabis online marketplace service platforms. MEASUREMENTS:This exploratory study was conducted in four phases: (1) data mining of cannabis e-commerce websites Weedmaps and Leafly for product listings in the US; (2) data filtering, text matching and content coding to identify types of advertised health benefit(s) made; (3) analysis on consumer-generated product reviews for sentiment toward advertised health benefit(s); and (4) ANOVA was used to test differences in mean number of health benefit claims based on product characteristic of route-of-administration (RoA). FINDINGS:A total of 624 805 unique US CDP sales listings from Leafly (n = 50 951) and Weedmaps (n = 573 854) were analyzed. CDP listings with a specific health benefit claim(s) were detected in 998 (1.9%) Leafly and 25 671 (4.47%) Weedmaps CDP listings. The top 5 advertised health benefits were treatment of mood disorders, general discomfort, general wellness, sleep disorders and chronic conditions. Among consumer reviews, 295 (4.6% of consumer reviews from products that advertised health benefit(s)) expressed sentiment toward CDP addressing their health issue with 82.4% being positive, 14.6% negative and 3.1% neutral. We also observed statistically significant differences between RoA and frequency of health benefit claims among those with at least one health benefit claim, with multisystem products (>1RoA) generally having a higher number of average health benefit claims compared with other RoAs. CONCLUSIONS:Over 26 000 cannabis-derived products listed on two popular US cannabis online marketplaces have at least one health benefit claim.

As radical messaging has proliferated on social networking sites, platforms like Reddit have been used to host support groups, including support communities for the families and friends of radicalized individuals. This study examines the subreddit r/QAnonCasualties, an online forum for users whose loved ones have been radicalized by QAnon. We collected 1,665 posts and 78,171 comments posted between 7/2021 and 7/2022 and content coded top posts for prominent themes. Sentiment analysis was also conducted on all posts. We find venting, advice and validation-seeking, and pressure to refuse the COVID-19 vaccine were prominent themes. 40 relation(s) of users as their parent(s) and 16.3 Posts with higher proportions of words related to swearing, social referents, and physical needs were positively correlated with engagement. These findings show ways that communities around QAnon adherents leverage anonymous online spaces to seek and provide social support.

BACKGROUND:Hikikomori syndrome is a form of severe social withdrawal prevalent in Japan but is also a worldwide psychiatric issue. Twitter (subsequently rebranded X) offers valuable insights into personal experiences with mental health conditions, particularly among isolated individuals or hard-to-reach populations. OBJECTIVE:This study aimed to examine trends in firsthand and secondhand experiences reported on Twitter between 2021 and 2023 in the Japanese language. METHODS:Tweets were collected using the Twitter academic research application programming interface filtered for the following keywords: "#きこもり," "#ひきこもり," "#hikikomori," "#ニート," "#ひきこもり," "#," and "#." The Bidirectional Encoder Representations From Transformers language model was used to analyze all Japanese-language posts collected. Themes and subthemes were then inductively coded for in-depth exploration of topic clusters relevant to first- and secondhand experiences with hikikomori syndrome. RESULTS:We collected 2,018,822 tweets, which were narrowed down to 379,265 (18.79%) tweets in Japanese from January 2021 to January 2023. After examining the topic clusters output by the Bidirectional Encoder Representations From Transformers model, 4 topics were determined to be relevant to the study aims. A total of 400 of the most highly interacted with tweets from these topic clusters were manually annotated for inclusion and exclusion, of which 148 (37%) tweets from 89 unique users were identified as relevant to hikikomori experiences. Of these 148 relevant tweets, 71 (48%) were identified as firsthand accounts, and 77 (52%) were identified as secondhand accounts. Within firsthand reports, the themes identified included seeking social support, personal anecdotes, debunking misconceptions, and emotional ranting. Within secondhand reports, themes included seeking social support, personal anecdotes, seeking and giving advice, and advocacy against the negative stigma of hikikomori. CONCLUSIONS:This study provides new insights into experiences reported by web-based users regarding hikikomori syndrome specific to Japanese-speaking populations. Although not yet found in diagnostic manuals classifying mental disorders, the rise of web-based lifestyles as a consequence of the COVID-19 pandemic has increased the importance of discussions regarding hikikomori syndrome in web-based spaces. The results indicate that social media platforms may represent a web-based space for those experiencing hikikomori syndrome to engage in social interaction, advocacy against stigmatization, and participation in a community that can be maintained through a web-based barrier and minimized sense of social anxiety.

Growing cannabis use has made it the most widely cultivated and trafficked illicit drug globally according to the World Health Organization, with 147 million people consuming cannabis-derived products (CDPs) in various product forms and constituency. Despite restrictions in certain countries, unregulated access can still be found on the dark web which specializes in trafficking of illicit goods. The objective was to systematically collect data from multiple marketplaces to identify types of cannabis products offered for sale. The study was conducted in three phases: (1) data mining transactions on dark web markets using cannabis and tobacco keywords; (2) inductive coding of selling-related characteristics; and (3) pricing analysis of one marketplace based on product type, shipping, and cannabis policy status. Four dark web markets (Archetyp, Incognito, Royal, and Wethenorth) yielded 2,954 selling posts. The top 3 products based on keyword searches included CDPs (n = 2629, 89%), illicit and prescription drugs (n = 223, 7.55%), and psychedelics (n = 102, 3.45%). For Archetyp listings, cannabis concentrates pricing had a statistically significant difference in average price p/mg when shipped from a country with a complete prohibition. The dark web represents an unregulated digital space where numerous CDPs are sold and shipped to various countries at different prices.

Public health diplomacy addresses global challenges impacting societies, economies, the environment, and health by integrating foreign policy and development. The University of Memphis School of Public Health hosted a multistakeholder summit to identify strategies and competencies essential for effective public health diplomacy. A 3-day summit included 29 participants from 15 countries, representing the WHO, the World Federation of United Nations, and seven regional public health associations. An iterative human-centered design (HCD) approach and concept mapping were employed to facilitate discussions and generate actionable recommendations. Developed a working definition of Public Health Diplomacy emphasizing cross-disciplinary collaborations, communication, negotiation, and consensus building. Produced a 9-point action plan to establish a global framework, launch capacity-building initiatives, and institutionalize public health diplomacy as a public health discipline.

In the digital era, health literacy is crucial for informed health decisions and improved outcomes.  This systematic review examines the effectiveness of digital health interventions (DHIs) in improving health literacy, as defined by the WHO. We included studies (cross-sectional, surveys, and case reports), focusing on interventions like mobile health apps, online platforms, and telehealth services. Our search, adhering to PRISMA guidelines, spanned databases PubMed, IEEE, and ACM, covering publications from 2013 to 2024. From 1.029 initial articles, 39 met our inclusion criteria.  Our findings highlight that DHIs, including multimedia tools and remote sessions, have been reported to improve health literacy across diverse populations. However, the impact varies due to the digital divide, influenced by factors like age and socioeconomic status. This review categorizes the included studies by digital intervention type, including: 10.3 % on mobile apps (n = 4), 30.8 % on websites and online platforms (n = 12), 5.1 % on multimedia tools (n = 2), 15.4 % on telehealth and mHealth (n = 6), 2.6 % on electronic health records and patient portals (n = 1), 17.9 % conference proceedings (n = 7), and 17.9 % review studies (n = 7). This study highlights the complex nature of DHIs designed to enhance health literacy and engagement, aiming to reduce health disparities and ensure equitable access to healthcare benefits regardless of socioeconomic background or digital literacy. It underscores the importance of user-centered design, cultural sensitivity, and ongoing support for maximizing the effectiveness of DHIs. Incorporating theoretical frameworks is shown to boost engagement and promote behavioral change, particularly by addressing intrinsic motivations and cultural factors. The findings emphasize the necessity of sustained strategies, such as gamification, to maintain improvements in health literacy, and advocate for standardized evaluation methods to guide policy and advance the global transition to digital-first healthcare. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement The author(s) received no specific funding for this work. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The source data for this review is drawn from the publications referenced in paragraph "3.2. Narrative Overview of the Studies Included" and detailed in Table 4. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The data supporting this article is presented in Table 4, which details the final 39 papers included in this review.

Throughout the COVID-19 pandemic, underserved populations, such as racial and ethnic minority communities, were disproportionately impacted by illness and death. Ensuring people from diverse backgrounds have the ability to participate in clinical trials is key to advancing health equity. We sought to analyze the spatial variability in locations of COVID-19 trials sites and to test associations with demographic correlates. All available and searchable COVID-19 studies listed on ClinicalTrials.gov until 04/04/2022 and conducted in the United States were extracted at the trial-level, and locations were geocoded using the Microsoft Bing API. Publicly available demographic data were available at the county level for national analysis and the census tract level for local analysis. Independent variables included eight racial and ethnic covariates, both sexes, and twelve age categories, all of which were population-normalized. The county-level, population-normalized count of study site locations, by type, was used as the outcome for national analysis, thereby enabling the determination of demographic associations with geospatial availability to enroll as a participant in a COVID-19 study. Z-scores of the Getis-Ord Gi statistic were used as the outcome for local analysis in order to account for areas close to those with clinical study sites. For both national (p < 0.001) and local analysis (p = 0.006 for Los Angeles, p = 0.030 for New York), areas with greater proportions of men had significantly fewer studies. Sites were more likely to be found in counties with higher proportions of Asian (p < 0.001) and American Indian or Alaska Native residents (p < 0.001). Areas with greater concentrations of Black or African American residents had significantly lower concentrations of observational (p < 0.001) and government-sponsored COVID-19 studies (p = 0.003) in national analysis and significantly fewer concentrations of study sites in both Los Angeles (p < 0.001) and New York (p = 0.007). Though there appear to be a large number of COVID-19 studies that commenced in the US, they are distributed unevenly, both nationally and locally.

Background E-cigarettes have emerged as popular products, especially for younger populations. However, concerns regarding health effects exist and there is a notable gap in understanding the prevalence and nature of adverse events. This study aims to examine the rate of adverse events in individuals who use e-cigarettes in a large sample.Methods A cross-sectional survey was conducted with a sample of 4695 current and former e-cigarette users with a median age of 34 years. The survey collected data on e-cigarette use, adverse events experienced, product characteristics, related behaviors, sociodemographic factors and presence of medical comorbidities. Statistical analyses were conducted using Pearson's chi-squared tests and logistic regression.Results A total of 78.9% of respondents reported experiencing an adverse event within 6 h of using a vaping device, with the most common events being headache, anxiety and coughing. Product characteristics and related behaviors significantly influenced the risk of adverse events. There were also sociodemographic disparities, with Hispanic respondents and those with at least college-level education reporting higher rates of adverse events.Conclusions Our study found a high rate of adverse events among e-cigarette users. We identified that certain e-cigarette product characteristics, behaviors and medical comorbidities significantly increased the risk of these events.

Background Many clinical trials fail because of poor recruitment and enrollment which can directly impact the success of biomedical and clinical research outcomes. Options to leverage digital technology for improving clinical trial management are expansive, with potential benefits for improving access to clinical trials, encouraging trial diversity and inclusion, and potential cost-savings through enhanced efficiency. Objectives This systematic review has two key aims: (1) identify and describe the digital technologies applied in clinical trial recruitment and enrollment and (2) evaluate evidence of these technologies addressing the recruitment and enrollment of racial and ethnic minority groups. Methods We conducted a cross-disciplinary review of articles from PubMed, IEEE Xplore, and ACM Digital Library, published in English between January 2012 and July 2022, using MeSH terms and keywords for digital health, clinical trials, and recruitment and enrollment. Articles unrelated to technology in the recruitment/enrollment process or those discussing recruitment/enrollment without technology aspects were excluded. Results The review returned 614 results, with 21 articles (four reviews and 17 original research articles) deemed suitable for inclusion after screening and full-text review. To address the first objective, various digital technologies were identified and characterized, which included articles with more than one technology subcategory including (a) multimedia presentations (19%, n = 4); (b) mobile applications (14%, n = 3); (c) social media platforms (29%, n = 6); (d) machine learning and computer algorithms (19%, n = 4); (e) e-consenting (24%, n = 5); (f) blockchain (5%, n = 1); (g) web-based programs (24%, n = 5); and (h) virtual messaging (24%, n = 5). Additionally, subthemes, including specific diseases or conditions addressed, privacy and regulatory concerns, cost/benefit analyses, and ethnic and minority recruitment considerations, were identified and discussed. Limited research was found to support a particular technology's effectiveness in racial and ethnic minority recruitment and enrollment. Conclusion Results from this review illustrate that several types of technology are currently being explored and utilized in clinical trial recruitment and enrollment stages. However, evidence supporting the use of digital technologies is varied and requires further research and evaluation to identify the most valuable opportunities for encouraging diversity in clinical trial recruitment and enrollment practices.

BackgroundExplanations for why social media users propagate misinformation include failure of classical reasoning (over-reliance on intuitive heuristics), motivated reasoning (conforming to group opinion), and personality traits (e.g., narcissism). However, there is a lack of consensus on which explanation is most predictive of misinformation spread. Previous work is also limited by not distinguishing between passive (i.e., “liking”) and active (i.e., “retweeting”) propagation behaviors.MethodsTo examine this issue, 858 Twitter users were recruited to engage in a Twitter simulation task in which they were shown real tweets on public health topics (e.g., COVID-19 vaccines) and given the option to “like”, “reply”, “retweet”, “quote”, or select “no engagement”. Survey assessments were then given to measure variables corresponding to explanations for: classical reasoning [cognitive reflective thinking (CRT)], motivated reasoning (religiosity, political conservatism, and trust in medical science), and personality traits (openness to new experiences, conscientiousness, empathy, narcissism).ResultsCognitive reflective thinking, conscientiousness, openness, and emotional concern empathy were all negatively associated with liking misinformation, but not significantly associated with retweeting it. Trust in medical scientists was negatively associated with retweeting misinformation, while grandiose narcissism and religiosity were positively associated. An exploratory analysis on engagement with misinformation corrections shows that conscientiousness, openness, and CRT were negatively associated with liking corrections while political liberalism, trust in medical scientists, religiosity, and grandiose narcissism were positively associated. Grandiose narcissism was the only factor positively associated with retweeting corrections.DiscussionFindings support an inhibitory role for classical reasoning in the passive spread of misinformation (e.g., “liking”), and a major role for narcissistic tendencies and motivated reasoning in active propagating behaviors (“retweeting”). Results further suggest differences in passive and active propagation, as multiple factors influence liking behavior while retweeting is primarily influenced by two factors. Implications for ecologically valid study designs are also discussed to account for greater nuance in social media behaviors in experimental research.

Introduction Unregulated and potentially illegal sales of tobacco, nicotine, and cannabis products have been detected on various social media platforms, e-commerce sites, online retailers, and the dark web. New end-to-end encrypted messaging services are popular among online users and present opportunities for marketing, trading, and selling of these products. The purpose of this study was to identify and characterize tobacco, nicotine, and cannabis selling activity on the messaging platform Telegram. Methods The study was conducted in three phases: (1) identifying keywords related to tobacco, nicotine, and cannabis products for purposes of detecting Telegram groups and channel messages; (2) automated data collection from public Telegram groups; and (3) manual annotation and classification of messages engaged in marketing and selling products to consumers. Results Four keywords were identified (“Nicotine,” “Vape,” “Cannabis,” and “Smoke”) that yielded 20 Telegram groups with 262 506 active subscribers. Total volume of channel messages was 43 963 unique messages that included 3094 (7.04%) marketing/selling messages. The most commonly sold products in these groups were cannabis-derived products (83.25%, n = 2576), followed by tobacco/nicotine-derived products (6.46%, n = 200), and other illicit drugs (0.77%, n = 24). A variety of marketing tactics and a mix of seller accounts were observed, though most appeared to be individual suppliers. Conclusions Telegram is an online messaging application that allows for custom group creation and global connectivity, but also includes unregulated activities associated with the sale of cannabis and nicotine delivery products. Greater attention is needed to conduct monitoring and enforcement on these emerging platforms for unregulated and potentially illegal cannabis and nicotine product sales direct-to-consumer. Implications Based on study results, Telegram represents an emerging platform that enables a robust cannabis and nicotine-selling marketplace. As local, state, and national tobacco control regulations continue to advance sales restrictions and bans at the retail level, easily accessible and unregulated Internet-based channels must be further assessed to ensure that they do not act as conduits for exposure and access to unregulated or illegal cannabis and nicotine products.

Background: During the COVID-19 pandemic, the rapid spread of misinformation on social media created significant publichealth challenges. Large language models (LLMs), pretrained on extensive textual data, have shown potential in detectingmisinformation, but their performance can be influenced by factors such as prompt engineering (ie, modifying LLM requests toassess changes in output). One form of prompt engineering is role-playing, where, upon request, OpenAI's ChatGPT imitatesspecific social roles or identities. This research examines how ChatGPT's accuracy in detecting COVID-19-related misinformationis affected when it is assigned social identities in the request prompt. Understanding how LLMs respond to different identity cuescan inform messaging campaigns, ensuring effective use in public health communications.Objective: This study investigates the impact of role-playing prompts on ChatGPT's accuracy in detecting misinformation.This study also assesses differences in performance when misinformation is explicitly stated versus implied, based on contextualknowledge, and examines the reasoning given by ChatGPT for classification decisions. Methods: Overall, 36 real-world tweets about COVID-19 collected in September 2021 were categorized into misinformation,sentiment (opinions aligned vs unaligned with public health guidelines), corrections, and neutral reporting. ChatGPT was testedwith prompts incorporating different combinations of multiple social identities (ie, political beliefs, education levels, locality,religiosity, and personality traits), resulting in 51,840 runs. Two control conditions were used to compare results: prompts withno identities and those including only political identity. Results: The findings reveal that including social identities in prompts reduces average detection accuracy, with a notable dropfrom 68.1% (SD 41.2%; no identities) to 29.3% (SD 31.6%; all identities included). Prompts with only political identity resultedin the lowest accuracy (19.2%, SD 29.2%). ChatGPT was also able to distinguish between sentiments expressing opinions notaligned with public health guidelines from misinformation making declarative statements. There were no consistent differencesin performance between explicit and implicit misinformation requiring contextual knowledge. While the findings show that theinclusion of identities decreased detection accuracy, it remains uncertain whether ChatGPT adopts views aligned with socialidentities: when assigned a conservative identity, ChatGPT identified misinformation with nearly the same accuracy as it didwhen assigned a liberal identity. While political identity was mentioned most frequently in ChatGPT's explanations for itsclassification decisions, the rationales for classifications were inconsistent across study conditions, and contradictory explanationswere provided in some instances.