JMIR infodemiology最新文献

Background: There has been a recent proliferation of anti-LGBTQ+ (lesbian, gay, bisexual, transgender, queer/questioning) legislation being proposed in the United States, including more than 500 bills across 42 states in 2024. Many of the studies examining the impact of anti-LGBTQ+ legislation have focused specifically on the association with mental health outcomes.

Objective: The objective of this study was to use social media data to more broadly explore health-related concerns of anti-LGBTQ+ legislation among sexual minority men in the United States.

Methods: We leveraged a dataset containing 70 million tweets that were posted by 23,276 users in the United States who self-reported on Twitter that they are sexual minority men. First, we searched these tweets for keywords related to LGBTQ+ legislation. Next, we developed a codebook for identifying those that expressed health-related concerns of anti-LGBTQ+ legislation. Then, we developed a coding scheme to categorize these concerns into one or more themes by using an inductive approach. Finally, we automatically identified the users' geographic location and age for subgroup analyses.

Results: Among 8486 keyword-matched tweets, 493 (5.8%) tweets expressed health-related concerns due to anti-LGBTQ+ legislation and were posted by 288 sexual minority men in the United States: 112 (38.9%) who posted about health care, 84 (29.2%) about safety, 64 (22.2%) about mental health, 62 (21.5%) about general harm, 49 (17%) about human rights, and 40 (13.9%) about support. Health care was the top concern overall and across the United States and age groups. In contrast, the higher prevalence of mental health was driven by the larger number of users in the South, as it was less of a concern in other regions. Similarly, mental health was less of a concern among older age groups. Safety was as much of a concern as mental health overall and across the United States and most age groups.

Conclusions: Our findings may inform a broader range of health interventions and approaches for targeting them at specific populations of sexual minority men. By demonstrating that these concerns are expressed on social media, our findings can be leveraged by advocacy groups to amplify voices and rally public support for countering anti-LGBTQ+ bills.

Background: 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.

Objective: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Background: 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.

Objective: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Background: 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.

Objective: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Background: 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.

Objective: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Background: 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.

Objective: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Background: 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.

Objective: 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.

Methods: 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.

Results: 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

Background: 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.

Objective: 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.

Methods: 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.

Results: 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.

Conclusions: 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.