JMIR infodemiology最新文献

{"title":"Data Mining Trauma: AI-Assisted Qualitative Study of Cyber Victimization on Reddit.","authors":"J'Andra Antisdel, Wendy R Miller, Doyle Groves","doi":"10.2196/75493","DOIUrl":"10.2196/75493","url":null,"abstract":"<p><strong>Background: </strong>Cyber victimization exposes individuals to numerous risks. Developmental and psychological factors may leave some users unaware of the potential dangers, increasing their susceptibility to psychological distress. Despite this vulnerability, methods for identifying those at risk of cyber victimization within health care settings are limited, as is research that explores their experiences of cyber victimization. The purpose of this study was to analyze how users describe experiences of cyber victimization on the social media platform Reddit (Reddit, Inc) using data mining.</p><p><strong>Objective: </strong>This study aimed to analyze and describe how users on Reddit describe and discuss their experience of cyber victimization using data mining and computational analysis of unsolicited data.</p><p><strong>Methods: </strong>This computational qualitative study used data mining, Word Adjacency Graph (WAG) modeling, and thematic analysis to analyze discussions of Reddit users surrounding cyber victimization. Inclusion criteria included posts from 2012 to 2023 from subreddits r/cyberbullying and r/bullying. GPT-4 (OpenAI), an advanced artificial intelligence language model, summarized posts and assisted in cluster labeling. Posts were reviewed to remove irrelevant content and duplicates. User anonymity was maintained throughout the study.</p><p><strong>Results: </strong>A total of 13,381 posts from 3283 Reddit were analyzed, with approximately 5.1% (n=678) originating between 2012 and 2018 and 94.9% (n=12,703) from 2019 to 2023. The WAG modeling approach identified 38 clusters, with 35 deemed to be relevant to cyber victimization experiences. Two clusters containing irrelevant material were excluded. Six overarching themes emerged: (1) psychological impact, (2) coping and healing, (3) protecting yourself online, (4) protecting yourself offline, (5) victimization across various settings, and (6) seeking meaning and understanding.</p><p><strong>Conclusions: </strong>The study highlights the effectiveness of data mining and AI in analyzing large public datasets for qualitative research. These methods can inform future studies on risky internet behavior, victimization, and assessment strategies in health care settings.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"5 ","pages":"e75493"},"PeriodicalIF":2.3,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12407219/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144994507","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":"Online Illicit Drug Distribution in the Thai Language on X: Exploratory Qualitative Content Analysis.","authors":"Francois Rene Lamy, Seung Chun Paek, Natthani Meemon","doi":"10.2196/71703","DOIUrl":"10.2196/71703","url":null,"abstract":"<p><strong>Background: </strong>By increasing exposure to drug-related advertisements, the illicit digital drug trade promotes drug normalization and eases access to substances, increasing the likelihood of initiation. Social media platforms play an increasingly important role in facilitating the online substance trade by leveraging encrypted communications and user-friendly interfaces to advertise a large variety of readily available substances. Despite its growing importance, there is a paucity of research conducted in Thailand that aims to determine the types of substances, marketing strategies, and public health risks linked to drugs advertised on social media.</p><p><strong>Objective: </strong>This study aimed to inductively explore the content of tweets on the social media platform X (formerly known as Twitter) advertising drugs in the Thai language.</p><p><strong>Methods: </strong>Tweets advertising psychoactive substances in the Thai language were collected manually between April and July 2024. A qualitative content analysis was performed on the collected tweets. Tweets were coded based on 5 themes: types of substances advertised, marketing strategies, delivery methods, number of substances per tweet, and location references. The intercoder reliability for each theme was assessed using Krippendorff α, achieving substantial agreement across most codes.</p><p><strong>Results: </strong>A total of 3832 tweets advertising drugs were collected and analyzed. Most tweets (2424/3832, 63.26%) mentioned 5 or more substances, with depressants such as opioids (2807/3832, 73.25%), antihistamines (2394/3832, 62.47%), and benzodiazepines (2009/3832, 52.42%) being the most frequently advertised. Common marketing techniques included direct contact information (2848/3832, 74.32%) and fast delivery (1216/3832, 31.73%). Delivery methods primarily involved courier services but generally offered multiple options. Tweets that mentioned at least 1 sex-performance enhancer were frequently (422/543, 77.7%) advertised in combination with benzodiazepine.</p><p><strong>Conclusions: </strong>The results of this study suggest the presence of a large number of substances advertised for sale on the X platform in the Thai language. This digital form of drug trading is facilitated by possible direct messaging and the large number of courier services existing in Thailand. Our findings call for the development of real-time monitoring systems that harness drug-related data from social media to inform public health practitioners about emerging substances and trends and address the challenges posed by the digital drug trade.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"5 ","pages":"e71703"},"PeriodicalIF":2.3,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12441646/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981328","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":"Mapping Vaccine Sentiment by Analyzing Spanish-Language Social Media Posts and Survey-Based Public Opinion: Dual Methods Study.","authors":"Agnes Huguet-Feixa, Wasim Ahmed, Eva Artigues-Barberà, Joaquim Sol, Xavier Gomez-Arbones, Pere Godoy, Marta Ortega Bravo","doi":"10.2196/63223","DOIUrl":"https://doi.org/10.2196/63223","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The internet and social media have been considered useful platforms for obtaining health information. However, critical and erroneous content about vaccines on social media has been associated with vaccination delays and refusal.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to examine how social networks influence access to and perceptions of vaccine-related information. We sought to (1) quantify the proportion of individuals engaging with vaccine-related content on social media and to characterize their demographic and behavioral profiles through an internet-based population survey conducted in Spain and (2) to analyze vaccine-related sentiments and opinions in Spanish and Catalan posts on X (X Corp [formerly Twitter, Inc] and geolocate them using artificial intelligence.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Two complementary methodologies were applied. First, an observational study was conducted via a self-administered internet-based questionnaire among adults in Spain in 2021. Second, we analyzed Spanish- and Catalan-language posts from X, collected between March and December 2021. Sentiment analysis was performed using a workflow developed in Orange Data Mining (Bioinformatics Laboratory, Faculty of Computer and Information Science, University of Ljubljana). Geolocation was based on user-defined locations and visualized using Microsoft Power Business Intelligence. Social network analysis was conducted with NodeXL Pro (Social Media Research Foundation) to identify and characterize the 5 largest user communities discussing vaccines. Although based on independent data sources, the 2 approaches provided complementary methodological insights.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Among the 1312 respondents in the survey, 85.7% (1124/1312) stated that they were regular social network users, and 66% (850/1287) reported having encountered antivaccine information on social networks. Of these, 24.3% (205/845) experienced doubts about receiving recommended vaccines, and out of those with doubts, 13.3% (27/203) refused at least 1 vaccine proposed by a health care professional. A total of 479,734 Spanish and Catalan posts on X were analyzed, with 54.44% (n=261,183) posts classified as negative, 28.18% (n=135,194) as neutral, and 17.37% (n=83,357) as positive. Sentiment varied across regions, with more negative posts appearing to derive from South America, with a mix in Europe and more positive posts in North America. Analysis of the topic words and key themes allowed the grouping of the predominant themes of the 5 study groups, which were (1) vaccination efforts during the COVID-19 pandemic, (2) issues of vaccine theft and struggles in managing and securing the vaccine supply, (3) campaigns in the State of Mexico, (4) vaccination efforts for older adults, and (5) the vaccination campaign in Colombia to combat COVID-19.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;High proportions of exposure to antivaccine content were reported by the su","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"5 ","pages":"e63223"},"PeriodicalIF":2.3,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12400125/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981280","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":"Communicating Antimicrobial Resistance on Instagram: Content Analysis of #AntibioticResistance.","authors":"Elin Nilsson, Emma Oljans, Anna-Carin Nordvall, Mirko Ancillotti","doi":"10.2196/67825","DOIUrl":"https://doi.org/10.2196/67825","url":null,"abstract":"<p><strong>Background: </strong>Antimicrobial resistance (AMR) is a major global health issue heavily influenced by human behavior. Effective communication and awareness-raising are crucial in curbing AMR, with social network sites (SNSs) significantly shaping health behaviors. Despite their potential, current analyses of AMR on SNSs have focused mainly on top-down communication initiatives.</p><p><strong>Objective: </strong>This study aims to examine AMR on Instagram (Meta Platforms), identifying key actors, content themes, and the nature of the communication to understand how AMR is portrayed and perceived.</p><p><strong>Methods: </strong>Based on the sender-message-channel-receiver model, this study used content analysis to review publicly accessible posts on Instagram. The data refer to 24 months, focusing on the hashtag \"#antibioticresistance.\" After cleaning the data, 610 posts (10% of the total 6105) were analyzed.</p><p><strong>Results: </strong>Content creators were predominantly information drivers or professionals in science and health. Posts frequently featured text-dominated visuals or images of bacteria and laboratory tests. However, the AMR posts were found to be siloed, with limited engagement beyond specific interest groups. The study highlighted the neutrality and accuracy of the content but noted the challenge of reaching a broader audience.</p><p><strong>Conclusions: </strong>While Instagram serves as a platform for accurate and informative AMR communication, the post of it remains confined to niche groups, limiting its broader impact. To enhance engagement, AMR discussions should be integrated into more general interest content, use visually compelling formats, and encourage institutional participation and interactive user engagement.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"5 ","pages":"e67825"},"PeriodicalIF":2.3,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12367282/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981322","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 Role of Influencers and Echo Chambers in the Diffusion of Vaccine Misinformation: Opinion Mining in a Taiwanese Online Community.","authors":"Jason Dean-Chen Yin, Tzu-Chin Wu, Chia-Yun Chen, Fen Lin, Xiaohui Wang","doi":"10.2196/57951","DOIUrl":"10.2196/57951","url":null,"abstract":"<p><strong>Background: </strong>Prevalence and spread of misinformation are a concern for the exacerbation of vaccine hesitancy and a resulting reduction in vaccine intent. However, few studies have focused on how vaccine misinformation diffuses online, who is responsible for the diffusion, and the mechanisms by which that happens. In addition, researchers have rarely investigated this in non-Western contexts particularly vulnerable to misinformation.</p><p><strong>Objective: </strong>This study aims to identify COVID-19 vaccine misinformation, map its diffusion, and identify the effect of echo chamber users on misinformation diffusion on a Taiwanese online forum.</p><p><strong>Methods: </strong>The study uses data from a popular forum in Taiwan called PTT. A crawler scraped all threads on the most popular subforum from January 2021 until December 2022. Vaccine-related threads were identified through keyword searching (n=5818). Types of misinformation, including misleading, disinformation, conspiracy, propaganda, and fabricated content, were coded by 2 researchers. Polarity was proposed as a proxy for measuring an individual's level of involvement in the echo chamber, one of the mechanisms responsible for the viral misinformation on social media. Factors related to information diffusion, including misinformation type and polarity, were then assessed with negative binomial regression.</p><p><strong>Results: </strong>Of 5818 threads, 3830 (65.8%) were identified as true information, and 1601 (27.5%) contained misinformation, yielding 5431 boards for analysis. Misinformation content did not vary much from other contexts. Propaganda-related information was most likely to be reposted (relative risk: 2.07; P<.001) when comparing to true information. However, the more polarized a user was, the less likely his or her content was to be reposted (relative risk: 0.22; P<.001). By removing the nodes with a high level of indegree, outdegree, and betweenness centrality, we found that the core network and the entire network demonstrated a decreasing trend in average polarity score, which showed that influential users contributed to the polarization in misinformation consumption.</p><p><strong>Conclusions: </strong>Although the forum exhibits a resilience to echo chambering, active users and brokers contribute significantly to the polarization of the community, particularly through propaganda-style misinformation. This popularity of propaganda-style misinformation may be linked to the political nature of the forum, where public opinion follows \"elite cues\" on issues, as observed in the United States. The work in this study corroborates this finding and contributes a data point in a non-Western context. To manage the echo chambering of misinformation, more effort can be put into moderating these users to prevent polarization and the spread of misinformation to prevent growing vaccine hesitancy.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"5 ","pages":"e57951"},"PeriodicalIF":2.3,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12360728/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144877147","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 Impact of Misinformation on Social Media in the Context of Natural Disasters: Narrative Review.","authors":"Sonya Hilberts, Mark Govers, Elena Petelos, Silvia Evers","doi":"10.2196/70413","DOIUrl":"10.2196/70413","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Misinformation on social media during natural disasters has become a significant challenge, with the potential to increase public confusion, panic, and distrust. Although individuals rely on social media platforms for timely updates during crises, these platforms also facilitate the rapid spread of unverified and misleading information. Consequently, misinformation can hamper emergency response efforts, misdirect resources, and distort public perception of the disaster's true severity.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This narrative review aims to (1) critically evaluate the available evidence; (2) unpack the dynamics of misinformation on social media in the context of natural disasters, specifically natural hazards, shedding light on the challenges, implications, and potential solutions; and (3) develop a conceptual model linking misinformation, public impact, and disasters, grounded in sourced evidence.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The narrative review examines the impact of social media misinformation in the context of natural disasters. The literature search was conducted using the PubMed database and Google Scholar in April 2024. Studies eligible for inclusion were published in English, with no restrictions on publication date, geographic region, or target population. The inclusion criteria focused on the original research that examined social media misinformation related to natural disasters, specifically natural hazards.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;From an initial pool of 173 studies, 9 studies met the inclusion criteria for this review. The selected studies revealed consistent patterns in how misinformation spreads during natural disasters, highlighting the role of users, some influencers, and bots in amplified false narratives. The misleading messages disseminated across social media platforms often outpaced official communications, resulting in reduced trust and exacerbating anxiety, stress, and fear among affected populations. This heightened emotional response and erosion of trust in official communications influenced an individual's susceptibility to the misinformation and prompted inappropriate actions. Consequently, such actions led to resource misallocation, overwhelmed emergency services, and diverted attention away from genuine needs. Collectively, these factors negatively impacted public health outcomes and diminished the effectiveness of emergency management efforts, as illustrated in the conceptual model developed to provide a greater understanding of this critical area of study.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This narrative review highlights the significant impact of misinformation in the context of natural disasters, specifically natural hazards. It stresses the urgent need for disaster preparedness and response plans that include targeted interventions such as real-time misinformation detection technologies, public education campaigns focused on digital literacy, and proactive d","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"5 ","pages":"e70413"},"PeriodicalIF":2.3,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12313155/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144762514","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":"Differential Analysis of Age, Gender, Race, Sentiment, and Emotion in Substance Use Discourse on Twitter During the COVID-19 Pandemic: A Natural Language Processing Approach.","authors":"Julina Maharjan, Ruoming Jin, Jennifer King, Jianfeng Zhu, Deric Kenne","doi":"10.2196/67333","DOIUrl":"10.2196/67333","url":null,"abstract":"<p><strong>Background: </strong>User demographics are often hidden in social media data due to privacy concerns. However, demographic information on substance use (SU) can provide valuable insights, allowing public health policy makers to focus on specific cohorts and develop efficient prevention strategies, especially during global crises such as the COVID-19 pandemic.</p><p><strong>Objective: </strong>This study aimed to analyze SU trends at the user level across different demographic dimensions, such as age, gender, race, and ethnicity, with a focus on the COVID-19 pandemic. The study also establishes a baseline for SU trends using social media data.</p><p><strong>Methods: </strong>The study was conducted using large-scale English-language data from Twitter (now known as X) over a 3-year period (2019, 2020, and 2021), comprising 1.13 billion posts. Following preprocessing, the SU posts were identified using our custom-trained deep learning model (Robustly Optimized Bidirectional Encoder Representations From Transformers Pretraining Approach [RoBERTa]), which resulted in the identification of 9 million SU posts. Then, demographic attributes, such as user type, age, gender, race, and ethnicity, as well as sentiments and emotions associated with each post, were extracted via a collection of natural language processing modules. Finally, various qualitative analyses were performed to obtain insight into user behaviors based on demographics.</p><p><strong>Results: </strong>The highest level of user participation in SU discussions was observed in 2020, with a 22.18% increase compared to 2019 and a 25.24% increase compared to 2021. Throughout the study period, male users and teenagers increasingly dominated the SU discussions across all substance types. During the COVID-19 pandemic, user participation in prescription medication discussions was notably higher among female users compared to other substance types. In addition, alcohol use increased by 80% within 2 weeks after the global pandemic declaration in 2020.</p><p><strong>Conclusions: </strong>This study presents a large-scale, fine-grained analysis of SU on social media data, examining trends by age, gender, race, and ethnicity before, during, and after the COVID-19 pandemic. Our findings, contextualized with sociocultural and pandemic-specific factors, provide actionable insights for targeted public health interventions. This study establishes social media data (powered with artificial intelligence and natural language processing tools) as a valuable platform for real-time SU surveillance and prevention during crises.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"5 ","pages":"e67333"},"PeriodicalIF":2.3,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12340460/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144735900","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":"Harnessing Facebook to Investigate Real-World Mentions of Adverse Events of Glucagon-Like Peptide-1 Receptor Agonist (GLP-1 RA) Medications: Observational Study of Facebook Posts From 2022 to 2024.","authors":"Amrutha S Alibilli, Vidur Jain, Heran Mane, Xiaohe Yue, Alexandria Ratzki-Leewing, Junaid S Merchant, Shaniece Criss, Quynh C Nguyen, Rozalina G McCoy, Thu T Nguyen","doi":"10.2196/73619","DOIUrl":"10.2196/73619","url":null,"abstract":"<p><strong>Background: </strong>In recent years, there has been a dramatic increase in the popularity and use of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) for weight loss. As such, it is essential to understand users' real-world discussions of short-term, long-term, and co-occurrent adverse events associated with currently used GLP-1 RA medications.</p><p><strong>Objective: </strong>This study aims to quantitatively analyze temporal and co-occurrent GLP-1 RA adverse event trends through discussions of GLP-1 RA weight loss medications on Facebook from 2022 to 2024.</p><p><strong>Methods: </strong>We collected 64,202 Facebook posts (59,293 posts after removing duplicate posts) from January 1, 2022, to May 31, 2024, through CrowdTangle, a public insights tool from Meta. Using English language social media posts from the United States, we examined discussions of adverse event mentions for posts referencing 7 GLP-1 RA weight loss product categories (ie, semaglutide, Ozempic, Wegovy, tirzepatide, Mounjaro, Zepbound, and GLP-1 RA as a class). All analyses were conducted using Python (version 3; Python Software Foundation) in a Google Colab environment.</p><p><strong>Results: </strong>Temporal time series analysis revealed that the GLP-1 RAs' adverse event mentions on social media aligned with several key events: the Food and Drug Administration's approval of Wegovy for pediatric weight management in December 2022, increased media coverage in August 2023, celebrity endorsement in December 2023, and Medicare Part D coverage expansion for weight loss medications in March 2024. Gastrointestinal (GI)-related adverse events (general term) were most prevalent for posts mentioning the GLP-1 RA class (210/4885, 4.30%) and Mounjaro (241/4031, 5.98%). In contrast, the most prevalent adverse event mentions noted for tirzepatide were headache (78/4202, 1.86%) and joint pain (71/4202, 1.69%). Hypertension (13/1769, 0.73%) was frequently mentioned in Zepbound posts, while pancreatitis was commonly associated with Mounjaro posts (44/4031, 1.08%), and 2.85% (139/4885) of posts broadly referring to the GLP-1 RA class. Furthermore, an integrated node network analysis revealed 3 distinct GLP-1 RA adverse events-mentioned clusters: cluster 1 (purple) contained allergies, anxiety, depression, chronic obstructive pulmonary disease, fatigue, fever, hypertension, indigestion, insomnia, gastroesophageal reflux disease, hives, swelling, restlessness, and seizures. Cluster 2 (pink) contained constipation, dehydration, headache, diarrhea, dizziness, hypoglycemia, sweating, and jaundice. Cluster 3 (brown) contained GI symptoms, such as nausea, pancreatitis, rash, and vomiting. The GI symptoms, such as nausea, vomiting, pancreatitis, diarrhea, and indigestion, were strongly associated together (≥100 co-occurrence mentions), while the mentioned neurological symptoms, such as anxiety, depression, and insomnia, were highly correlated with each other (50-100 co-occurrence men","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"5 ","pages":"e73619"},"PeriodicalIF":2.3,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12289294/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144710087","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 Quality and Reliability of Online Videos as an Information Source of Public Health Education for Stroke Prevention in Mainland China: Electronic Media-Based Cross-Sectional Study.","authors":"Rongguang Ge, Haoyi Dai, Chicheng Gong, Yuhong Xia, Rui Wang, Jiaping Xu, Shoujiang You, Yongjun Cao","doi":"10.2196/64891","DOIUrl":"10.2196/64891","url":null,"abstract":"<p><strong>Background: </strong>Stroke has become a leading cause of death and disability worldwide, resulting in a significant loss of healthy life years and imposing a considerable economic burden on patients, their families, and caregivers. However, despite the growing role of online videos as an emerging source of health information, the credibility and quality of stroke prevention education videos, especially those in Chinese, remain unclear.</p><p><strong>Objective: </strong>This study aims to assess the basic characteristics, overall quality, and reliability of Chinese-language online videos related to public health education on stroke prevention.</p><p><strong>Methods: </strong>We systematically searched and screened stroke prevention-related video resources from 4 popular Chinese domestic video platforms (Bilibili, Douyin, Haokan, and Xigua). General information, including upload date, duration, views, likes, comments, and shares, was extracted and recorded. Two validated evaluation tools were used: the modified DISCERN questionnaire to assess content reliability and the Global Quality Scale (GQS) to evaluate overall quality. Finally, Spearman correlation analysis was conducted to examine potential associations between general video metrics and their quality and reliability.</p><p><strong>Results: </strong>After searching and screening, a total of 313 eligible videos were included for analysis: 68 from Bilibili, 74 from Douyin, 86 from Haokan, and 85 from Xigua. Among these, 113 (36.1%) were created by health care professionals, followed by news agencies (n=95, 30.4%) and general individual users (n=40, 12.8%). The median scores for the modified DISCERN and GQS were 2 and 3, respectively, suggesting that the included stroke prevention-related videos were relatively unreliable and of moderate quality. Most videos focused on primary stroke prevention and commonly recommended adopting a healthy diet; engaging in physical activity; and managing blood pressure, glucose, and lipid levels. Additionally, videos with longer durations and more comments tended to be more reliable and of higher quality. A positive association was also observed between video quality and reliability.</p><p><strong>Conclusions: </strong>Overall, the quality and reliability of Chinese-language online videos as a source of stroke prevention information remain unsatisfactory and should be approached with caution by viewers. To address this issue, several measures should be implemented, including establishing an online monitoring and correction system, strengthening the video review process through collaboration with health care professionals, and encouraging more selective and cautious sharing of controversial content. These steps are essential to help curb the spread of online misinformation and minimize its ongoing impact.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"5 ","pages":"e64891"},"PeriodicalIF":2.3,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12303359/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144683710","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":"Using Natural Language Processing to Describe the Use of an Online Community for Abortion During 2022: Dynamic Topic Modeling Analysis of Reddit Posts.","authors":"Elizabeth Pleasants, Ndola Prata, Ushma D Upadhyay, Cassondra Marshall, Coye Cheshire","doi":"10.2196/72771","DOIUrl":"10.2196/72771","url":null,"abstract":"<p><strong>Background: </strong>Abortion access in the United States has been in a state of rapid change and increasing restriction since the Dobbs v Jackson Women's Health Organization decision from the US Supreme Court in June 2022. With further constraints on access to abortion since Dobbs, the internet and online communities are playing an increasingly important role in people's abortion trajectories. There is a need for a broader understanding of how online resources are used for abortion and how they may reflect changes in the sociopolitical and legal context of abortion access. Research using online information and leveraging methods to work efficiently with large textual datasets has the potential to accelerate knowledge generation and provide novel insights into changing abortion-related experiences following Dobbs, helping address these knowledge gaps.</p><p><strong>Objective: </strong>This project sought to use natural language processing techniques, specifically topic modeling, to explore the content of posts to 1 online community for abortion (r/abortion) in 2022 and assess how community use changed during that time.</p><p><strong>Methods: </strong>This analysis described and explored posts shared throughout 2022 and for 3 subperiods of interest: before the Dobbs leak (December 24, 2021-May 1, 2022), Dobbs leak to decision (May 2, 2022-June 23, 2022), and after the Dobbs decision (June 24, 2022-December 23, 2022). We used topic modeling to obtain descriptive topics for the year and each subperiod and then classified posts. Topics were then aggregated into conceptual groups based on a combination of quantitative and qualitative assessments. The proportion of posts classified in each conceptual group was used to assess change in community interests across the 3 study subperiods.</p><p><strong>Results: </strong>The 7273 posts shared in r/abortion in 2022 included in our analyses were categorized into 8 conceptual groups: abortion decision-making, navigating abortion access barriers, clinical abortion care, medication abortion processes, postabortion physical experiences, potential pregnancy, and self-managed abortion processes. Posts related to navigating access barriers were most common. The proportion of posts about abortion decision-making and self-management changed significantly across study periods (P=.006 and P<.001, respectively); abortion decision-making posts were more common before the Dobbs leak, whereas those related to self-management increased following the leak and decision.</p><p><strong>Conclusions: </strong>This analysis provides a holistic view of r/abortion posts in 2022, highlighting the important role of online communities as abortion-supportive online resources and changing interests among posters with abortion policy changes. As policies and pathways to abortion access continue to change across the United States, approaches leveraging natural language processing with sufficiently large samples of textual data pr","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"5 ","pages":"e72771"},"PeriodicalIF":2.3,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12287674/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144602445","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}