JMIR infodemiology最新文献

{"title":"Understanding and Combating Misinformation: An Evolutionary Perspective.","authors":"Nicola Luigi Bragazzi, Sergio Garbarino","doi":"10.2196/65521","DOIUrl":"10.2196/65521","url":null,"abstract":"<p><p>Misinformation represents an evolutionary paradox: despite its harmful impact on society, it persists and evolves, thriving in the information-rich environment of the digital age. This paradox challenges the conventional expectation that detrimental entities should diminish over time. The persistence of misinformation, despite advancements in fact-checking and verification tools, suggests that it possesses adaptive qualities that enable it to survive and propagate. This paper explores how misinformation, as a blend of truth and fiction, continues to resonate with audiences. The role of narratives in human history, particularly in the evolution of Homo narrans, underscores the enduring influence of storytelling on cultural and social cohesion. Despite the increasing ability of individuals to verify the accuracy of sources, misinformation remains a significant challenge, often spreading rapidly through digital platforms. Current behavioral research tends to treat misinformation as completely irrational, static, finite entities that can be definitively debunked, overlooking their dynamic and evolving nature. This approach limits our understanding of the behavioral and societal factors driving the transformation of misinformation over time. The persistence of misinformation can be attributed to several factors, including its role in fostering social cohesion, its perceived short-term benefits, and its use in strategic deception. Techniques such as extrapolation, intrapolation, deformation, cherry-picking, and fabrication contribute to the production and spread of misinformation. Understanding these processes and the evolutionary advantages they confer is crucial for developing effective strategies to counter misinformation. By promoting transparency, critical thinking, and accurate information, society can begin to address the root causes of misinformation and create a more resilient information environment.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":" ","pages":"e65521"},"PeriodicalIF":3.5,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11724212/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142514189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Navigating Awareness and Strategies to Support Dementia Advocacy on Social Media During World Alzheimer's Month: Infodemiology Study.","authors":"Juanita-Dawne Bacsu, Sarah Anne Fraser, Ali Akbar Jamali, Christine Conanan, Alison L Chasteen, Shirin Vellani, Rory Gowda-Sookochoff, Corinne Berger, Jasmine C Mah, Florriann Fehr, Anila Virani, Zahra Rahemi, Kate Nanson, Allison Cammer, Melissa K Andrew, Karl S Grewal, Katherine S McGilton, Samantha Lautrup, Raymond J Spiteri","doi":"10.2196/63464","DOIUrl":"10.2196/63464","url":null,"abstract":"<p><strong>Background: </strong>Understanding advocacy strategies is essential to improving dementia awareness, reducing stigma, supporting cognitive health promotion, and influencing policy to support people living with dementia. However, there is a dearth of evidence-based research on advocacy strategies used to support dementia awareness.</p><p><strong>Objective: </strong>This study aimed to use posts from X (formerly known as Twitter) to understand dementia advocacy strategies during World Alzheimer's Awareness Month in September 2022.</p><p><strong>Methods: </strong>Posts were scraped from X during World Alzheimer's Awareness Month from September 1, 2022, to September 30, 2022. After applying filters, 1981 relevant posts were analyzed using thematic analysis, and measures were taken to support trustworthiness and rigor.</p><p><strong>Results: </strong>Our study revealed a variety of advocacy strategies, including sharing the voices of lived experience, targeting ethnic and cultural groups, myth-busting strategies, and political lobbying. Although a range of strategies were identified, further research is needed to examine advocacy strategies within different countries and political contexts. Furthermore, the impact of specific strategies on stigma reduction, cognitive health promotion, and policy change needs to be scientifically evaluated.</p><p><strong>Conclusions: </strong>Our study offers valuable insight into strategies to bolster dementia advocacy and awareness campaigns to support people living with dementia. Findings from our research may provide critical insight for policymakers, organizations, and health professionals working to reduce dementia-related stigma and increase the uptake of risk-reduction activities to support the promotion of cognitive health.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"4 ","pages":"e63464"},"PeriodicalIF":3.5,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11724219/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142900980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of a Language Model Tool for COVID-19 Vaccine Adverse Event Monitoring Using Web and Social Media Content: Algorithm Development and Validation Study.","authors":"Chathuri Daluwatte, Alena Khromava, Yuning Chen, Laurence Serradell, Anne-Laure Chabanon, Anthony Chan-Ou-Teung, Cliona Molony, Juhaeri Juhaeri","doi":"10.2196/53424","DOIUrl":"10.2196/53424","url":null,"abstract":"<p><strong>Background: </strong>Spontaneous pharmacovigilance reporting systems are the main data source for signal detection for vaccines. However, there is a large time lag between the occurrence of an adverse event (AE) and the availability for analysis. With global mass COVID-19 vaccination campaigns, social media, and web content, there is an opportunity for real-time, faster monitoring of AEs potentially related to COVID-19 vaccine use. Our work aims to detect AEs from social media to augment those from spontaneous reporting systems.</p><p><strong>Objective: </strong>This study aims to monitor AEs shared in social media and online support groups using medical context-aware natural language processing language models.</p><p><strong>Methods: </strong>We developed a language model-based web app to analyze social media, patient blogs, and forums (from 190 countries in 61 languages) around COVID-19 vaccine-related keywords. Following machine translation to English, lay language safety terms (ie, AEs) were observed using the PubmedBERT-based named-entity recognition model (precision=0.76 and recall=0.82) and mapped to Medical Dictionary for Regulatory Activities (MedDRA) terms using knowledge graphs (MedDRA terminology is an internationally used set of terms relating to medical conditions, medicines, and medical devices that are developed and registered under the auspices of the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use). Weekly and cumulative aggregated AE counts, proportions, and ratios were displayed via visual analytics, such as word clouds.</p><p><strong>Results: </strong>Most AEs were identified in 2021, with fewer in 2022. AEs observed using the web app were consistent with AEs communicated by health authorities shortly before or within the same period.</p><p><strong>Conclusions: </strong>Monitoring the web and social media provides opportunities to observe AEs that may be related to the use of COVID-19 vaccines. The presented analysis demonstrates the ability to use web content and social media as a data source that could contribute to the early observation of AEs and enhance postmarketing surveillance. It could help to adjust signal detection strategies and communication with external stakeholders, contributing to increased confidence in vaccine safety monitoring.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"4 ","pages":"e53424"},"PeriodicalIF":3.5,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11699502/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142866461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Changes in Reproductive Health Information-Seeking Behaviors After the Dobbs Decision: Systematic Search of the Wikimedia Database.","authors":"Mackenzie Lemieux, Cyrus Zhou, Caroline Cary, Jeannie Kelly","doi":"10.2196/64577","DOIUrl":"10.2196/64577","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;After the US Supreme Court overturned Roe v. Wade, confusion followed regarding the legality of abortion in different states across the country. Recent studies found increased Google searches for abortion-related terms in restricted states after the Dobbsv. Jackson Women's Health Organization decision was leaked. As patients and providers use Wikipedia (Wikimedia Foundation) as a predominant medical information source, we hypothesized that changes in reproductive health information-seeking behavior could be better understood by examining Wikipedia article traffic.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to examine trends in Wikipedia usage for abortion and contraception information before and after the Dobbs decision.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Page views of abortion- and contraception-related Wikipedia pages were scraped. Temporal changes in page views before and after the Dobbs decision were then analyzed to explore changes in baseline views, differences in views for abortion-related information in states with restrictive abortion laws versus nonrestrictive states, and viewer trends on contraception-related pages.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Wikipedia articles related to abortion topics had significantly increased page views following the leaked and final Dobbs decision. There was a 103-fold increase in the page views for the Wikipedia article Roe v. Wade following the Dobbs decision leak (mean 372,654, SD 135,478 vs mean 3614, SD 248; P&lt;.001) and a 67-fold increase in page views following the release of the final Dobbs decision (mean 8942, SD 402 vs mean 595,871, SD 178,649; P&lt;.001). Articles about abortion in the most restrictive states had a greater increase in page views (mean 40.6, SD 12.7; 18/51, 35% states) than articles about abortion in states with some restrictions or protections (mean 26.8, SD 7.3; 24/51, 47% states; P&lt;.001) and in the most protective states (mean 20.6, SD 5.7; 8/51, 16% states; P&lt;.001). Finally, views to pages about common contraceptive methods significantly increased after the Dobbs decision. \"Vasectomy\" page views increased by 183% (P&lt;.001), \"IUD\" (intrauterine device) page views increased by 80% (P&lt;.001), \"Combined oral contraceptive pill\" page views increased by 24% (P&lt;.001), \"Emergency Contraception\" page views increased by 224% (P&lt;.001), and \"Tubal ligation\" page views increased by 92% (P&lt;.001).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;People sought information on Wikipedia about abortion and contraception at increased rates after the Dobbs decision. Increased traffic to abortion-related Wikipedia articles correlated to the restrictiveness of state abortion policies. Increased interest in contraception-related pages reflects the increased demand for contraceptives observed after the Dobbs decision. Our work positions Wikipedia as an important source of reproductive health information and demands increased attention to maintain and improve Wikipedia as a reliable s","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"4 ","pages":"e64577"},"PeriodicalIF":3.5,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11686025/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142840477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Complex Interaction Between Sleep-Related Information, Misinformation, and Sleep Health: Call for Comprehensive Research on Sleep Infodemiology and Infoveillance.","authors":"Nicola Luigi Bragazzi, Sergio Garbarino","doi":"10.2196/57748","DOIUrl":"10.2196/57748","url":null,"abstract":"<p><p>The complex interplay between sleep-related information-both accurate and misleading-and its impact on clinical public health is an emerging area of concern. Lack of awareness of the importance of sleep, and inadequate information related to sleep, combined with misinformation about sleep, disseminated through social media, nonexpert advice, commercial interests, and other sources, can distort individuals' understanding of healthy sleep practices. Such misinformation can lead to the adoption of unhealthy sleep behaviors, reducing sleep quality and exacerbating sleep disorders. Simultaneously, poor sleep itself impairs critical cognitive functions, such as memory consolidation, emotional regulation, and decision-making. These impairments can heighten individuals' vulnerability to misinformation, creating a vicious cycle that further entrenches poor sleep habits and unhealthy behaviors. Sleep deprivation is known to reduce the ability to critically evaluate information, increase suggestibility, and enhance emotional reactivity, making individuals more prone to accepting persuasive but inaccurate information. This cycle of misinformation and poor sleep creates a clinical public health issue that goes beyond individual well-being, influencing occupational performance, societal productivity, and even broader clinical public health decision-making. The effects are felt across various sectors, from health care systems burdened by sleep-related issues to workplaces impacted by decreased productivity due to sleep deficiencies. The need for comprehensive clinical public health initiatives to combat this cycle is critical. These efforts must promote sleep literacy, increase awareness of sleep's role in cognitive resilience, and correct widespread sleep myths. Digital tools and technologies, such as sleep-tracking devices and artificial intelligence-powered apps, can play a role in educating the public and enhancing the accessibility of accurate, evidence-based sleep information. However, these tools must be carefully designed to avoid the spread of misinformation through algorithmic biases. Furthermore, research into the cognitive impacts of sleep deprivation should be leveraged to develop strategies that enhance societal resilience against misinformation. Sleep infodemiology and infoveillance, which involve tracking and analyzing the distribution of sleep-related information across digital platforms, offer valuable methodologies for identifying and addressing the spread of misinformation in real time. Addressing this issue requires a multidisciplinary approach, involving collaboration between sleep scientists, health care providers, educators, policy makers, and digital platform regulators. By promoting healthy sleep practices and debunking myths, it is possible to disrupt the feedback loop between poor sleep and misinformation, leading to improved individual health, better decision-making, and stronger societal outcomes.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":" ","pages":"e57748"},"PeriodicalIF":3.5,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11681283/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142549354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncovering the Top Nonadvertising Weight Loss Websites on Google: A Data-Mining Approach.","authors":"Carlos A Almenara, Hayriye Gulec","doi":"10.2196/51701","DOIUrl":"10.2196/51701","url":null,"abstract":"<p><strong>Background: </strong>Online weight loss information is commonly sought by internet users, and it may impact their health decisions and behaviors. Previous studies examined a limited number of Google search queries and relied on manual approaches to retrieve online weight loss websites.</p><p><strong>Objective: </strong>This study aimed to identify and describe the characteristics of the top weight loss websites on Google.</p><p><strong>Methods: </strong>This study gathered 432 Google search queries collected from Google autocomplete suggestions, \"People Also Ask\" featured questions, and Google Trends data. A data-mining software tool was developed to retrieve the search results automatically, setting English and the United States as the default criteria for language and location, respectively. Domain classification and evaluation technologies were used to categorize the websites according to their content and determine their risk of cyberattack. In addition, the top 5 most frequent websites in nonadvertising (ie, nonsponsored) search results were inspected for quality.</p><p><strong>Results: </strong>The results revealed that the top 5 nonadvertising websites were healthline.com, webmd.com, verywellfit.com, mayoclinic.org, and womenshealthmag.com. All provided accuracy statements and author credentials. The domain categorization taxonomy yielded a total of 101 unique categories. After grouping the websites that appeared less than 5 times, the most frequent categories involved \"Health\" (104/623, 16.69%), \"Personal Pages and Blogs\" (91/623, 14.61%), \"Nutrition and Diet\" (48/623, 7.7%), and \"Exercise\" (34/623, 5.46%). The risk of being a victim of a cyberattack was low.</p><p><strong>Conclusions: </strong>The findings suggested that while quality information is accessible, users may still encounter less reliable content among various online resources. Therefore, better tools and methods are needed to guide users toward trustworthy weight loss information.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"4 ","pages":"e51701"},"PeriodicalIF":3.5,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11669867/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142815147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association Between X/Twitter and Prescribing Behavior During the COVID-19 Pandemic: Retrospective Ecological Study.","authors":"Scott A Helgeson, Rohan M Mudgalkar, Keith A Jacobs, Augustine S Lee, Devang Sanghavi, Pablo Moreno Franco, Ian S Brooks","doi":"10.2196/56675","DOIUrl":"10.2196/56675","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Social media has become a vital tool for health care providers to quickly share information. However, its lack of content curation and expertise poses risks of misinformation and premature dissemination of unvalidated data, potentially leading to widespread harmful effects due to the rapid and large-scale spread of incorrect information.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;We aim to determine whether social media had an undue association with the prescribing behavior of hydroxychloroquine, using the COVID-19 pandemic as the setting.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;In this retrospective study, we gathered the use of hydroxychloroquine in 48 hospitals in the United States between January and December 2020. Social media data from X/Twitter was collected using Brandwatch, a commercial aggregator with access to X/Twitter's data, and focused on mentions of \"hydroxychloroquine\" and \"Plaquenil.\" Tweets were categorized by sentiment (positive, negative, or neutral) using Brandwatch's sentiment analysis tool, with results classified by date. Hydroxychloroquine prescription data from the National COVID Cohort Collaborative for 2020 was used. Granger causality and linear regression models were used to examine relationships between X/Twitter mentions and prescription trends, using optimum time lags determined via vector auto-regression.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 581,748 patients with confirmed COVID-19 were identified. The median daily number of positive COVID-19 cases was 1318.5 (IQR 1005.75-1940.3). Before the first confirmed COVID-19 case, hydroxychloroquine was prescribed at a median rate of 559 (IQR 339.25-728.25) new prescriptions per day. A day-of-the-week effect was noted in both prescriptions and case counts. During the pandemic in 2020, hydroxychloroquine prescriptions increased significantly, with a median of 685.5 (IQR 459.75-897.25) per day, representing a 22.6% rise from baseline. The peak occurred on April 2, 2020, with 3411 prescriptions, a 397.6% increase. Hydroxychloroquine mentions on X/Twitter peaked at 254,770 per day on April 5, 2020, compared to a baseline of 9124 mentions per day before January 21, 2020. During this study's period, 3,823,595 total tweets were recorded, with 10.09% (n=386,115) positive, 37.87% (n=1,448,030) negative, and 52.03% (n=1,989,450) neutral sentiments. A 1-day lag was identified as the optimal time for causal association between tweets and hydroxychloroquine prescriptions. Univariate analysis showed significant associations across all sentiment types, with the largest impact from positive tweets. Multivariate analysis revealed only neutral and negative tweets significantly affected next-day prescription rates.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;During the first year of the COVID-19 pandemic, there was a significant association between X/Twitter mentions and the number of prescriptions of hydroxychloroquine. This study showed that X/Twitter has an association with ","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"4 ","pages":"e56675"},"PeriodicalIF":3.5,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11612580/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142649753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Exploring the Impact of the COVID-19 Pandemic on Twitter in Japan: Qualitative Analysis of Disrupted Plans and Consequences.","authors":"Masaru Kamba, Wan Jou She, Kiki Ferawati, Shoko Wakamiya, Eiji Aramaki","doi":"10.2196/67434","DOIUrl":"10.2196/67434","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.2196/49699.].</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"4 ","pages":"e67434"},"PeriodicalIF":4.3,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11558209/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142549355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection and Characterization of Online Substance Use Discussions Among Gamers: Qualitative Retrospective Analysis of Reddit r/StopGaming Data.","authors":"Nicolette Le, Tiana McMann, Luning Yang, Zhuoran Li, Raphael E Cuomo, Tim K Mackey","doi":"10.2196/58201","DOIUrl":"10.2196/58201","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Video games have rapidly become mainstream in recent decades, with over half of the US population involved in some form of digital gaming. However, concerns regarding the potential harms of excessive, disordered gaming have also risen. Internet gaming disorder (IGD) has been proposed as a tentative psychiatric disorder that requires further study by the American Psychological Association (APA) and is recognized as a behavioral addiction by the World Health Organization. Substance use among gamers has also become a concern, with caffeinated or energy drinks and prescription stimulants commonly used for performance enhancement.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to identify substance use patterns and health-related concerns among gamers among a population of Reddit users.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We used the public streaming Reddit application programming interface to collect and analyze all posts from the popular subreddit, r/StopGaming. From this corpus of posts, we filtered the dataset for keywords associated with common substances that may be used to enhance gaming performance. We then applied an inductive coding approach to characterize substance use behaviors, gaming genres, and physical and mental health concerns. Potential disordered gaming behavior was also identified using the tentative IGD guidelines proposed by the APA. A chi-square test of independence was used to assess the association between gaming disorder and substance use characteristics, and multivariable logistic regression was used to analyze whether mental health discussion or the mention of any substance with sufficient sample size was significantly associated with IGD.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;In total, 10,551 posts were collected from Reddit from June 2017 to December 2022. After filtering the dataset for substance-related keywords, 1057 were included for further analysis, of which 286 mentioned both gaming and the use of ≥1 substances. Among the 286 posts that discussed both gaming and substance use, the most mentioned substances were alcohol (n=132), cannabis (n=104), and nicotine (n=48), while the most mentioned genres were role-playing games (n=120), shooters (n=90), and multiplayer online battle arenas (n=43). Self-reported behavior that aligned with the tentative guidelines for IGD was identified in 66.8% (191/286) posts. More than half, 62.9% (180/286) of the posts, discussed a health issue, with the majority (n=144) cited mental health concerns. Common mental health concerns discussed were depression and anxiety. There was a significant association between IGD and substance use (P&lt;.001; chi-square test), and there were significantly increased odds of IGD among those who self-reported substance use (odds ratio 2.29, P&lt;.001) and those who discussed mental health (odds ratio 1.64, P&lt;.03).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;As gaming increasingly becomes highly prevalent among various age groups and demogra","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"4 ","pages":"e58201"},"PeriodicalIF":3.5,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11483252/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142367743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the Influence of Role-Playing Prompts on ChatGPT's Misinformation Detection Accuracy: Quantitative Study.","authors":"Michael Robert Haupt, Luning Yang, Tina Purnat, Tim Mackey","doi":"10.2196/60678","DOIUrl":"10.2196/60678","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;During the COVID-19 pandemic, the rapid spread of misinformation on social media created significant public health challenges. Large language models (LLMs), pretrained on extensive textual data, have shown potential in detecting misinformation, but their performance can be influenced by factors such as prompt engineering (ie, modifying LLM requests to assess changes in output). One form of prompt engineering is role-playing, where, upon request, OpenAI's ChatGPT imitates specific social roles or identities. This research examines how ChatGPT's accuracy in detecting COVID-19-related misinformation is affected when it is assigned social identities in the request prompt. Understanding how LLMs respond to different identity cues can inform messaging campaigns, ensuring effective use in public health communications.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;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 contextual knowledge, and examines the reasoning given by ChatGPT for classification decisions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;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 tested with 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 with no identities and those including only political identity.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The findings reveal that including social identities in prompts reduces average detection accuracy, with a notable drop from 68.1% (SD 41.2%; no identities) to 29.3% (SD 31.6%; all identities included). Prompts with only political identity resulted in the lowest accuracy (19.2%, SD 29.2%). ChatGPT was also able to distinguish between sentiments expressing opinions not aligned with public health guidelines from misinformation making declarative statements. There were no consistent differences in performance between explicit and implicit misinformation requiring contextual knowledge. While the findings show that the inclusion of identities decreased detection accuracy, it remains uncertain whether ChatGPT adopts views aligned with social identities: when assigned a conservative identity, ChatGPT identified misinformation with nearly the same accuracy as it did when assigned a liberal identity. While political identity was mentioned most frequently in ChatGPT's explanations for its classification decisions, the rationales for classifications were inconsistent across study conditions, and contradictory explanations were provided in some instances.&lt;/","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"4 ","pages":"e60678"},"PeriodicalIF":3.5,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11467603/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142333891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}