JMIR infodemiology最新文献

Background: Dengue fever has evolved into a significant public health concern. In recent years, short-video platforms such as Douyin have emerged as prominent media for the dissemination of health education content. Nevertheless, there is a paucity of research investigating the quality of health education content on Douyin.

Objective: This study aimed to evaluate the quality of dengue videos on Douyin.

Methods: A comprehensive collection of short videos pertaining to dengue fever was retrieved from the popular social media platform, Douyin, at a designated point in time. A systematic analysis was then performed to extract the characteristics of these videos. To ensure a comprehensive evaluation, three distinct scoring tools were used: the DISCERN scoring tool, the JAMA benchmarking criteria, and the GQS method. Subsequently, an in-depth investigation was undertaken into the relationship between video features and quality.

Results: A total of 156 videos were included in the analysis, 81 of which (51.9%) were posted by physicians, constituting the most active category of contributor. The selected videos pertaining to dengue fever received a total of 718,228 likes and 126,400 comments. The video sources were categorized into four distinct classifications: news agencies, organizations, physicians, and individuals. Individuals obtained the highest number of video likes, comments, and saves. However, the findings of the study demonstrated that physicians, organizations, and news agencies posted videos are of higher quality when compared with individuals. The integrity of the video content was analyzed, and the results showed a higher percentage of videos received a score of zero points for outcomes, management, and assessment, with 69 (45%), 57 (37%), and 41 (26%), respectively. The median Total DISCERN scores, JAMA, and GQS of the 156 dengue-related videos under consideration were 26 (out of a total of 80 points), 2 (out of a total of 4 points), and 3 (out of a total of 5 points), respectively. Spearman correlation analysis was conducted, revealing a positive correlation between video duration and video quality. Conversely, a negative correlation was observed between the following variables: video comments and video quality, and the number of days since posting and video quality.

Conclusions: This study demonstrates that the quality of short dengue-related health information videos on Douyin is substandard. Videos uploaded by medical professionals were among the highest in terms of quality, yet their videos were not as popular. It is recommended that in future, physicians employ more accessible language incorporating visual elements to enhance the appeal and dissemination of their videos. Future research could explore how to achieve a balance between professionalism and entertainment to promote user acceptance of high-quality content. Moreov

Background: Chronic pain, affecting 30.3% of the global population, constitutes a major public health and social challenge. It is associated with disability, emotional distress, and diminished quality of life. Conditions, such as fibromyalgia, headache, paraplegia, neuropathy, and multiple sclerosis are characterized by persistent pain and limited social and medical understanding. This contributes to patient isolation and increases mental health burden. In recent years, social media, particularly X (formerly Twitter), has emerged as a key space for analyzing health-related perceptions and experiences. Its massive use, spontaneity, and broad reach have made these platforms a valuable source for infodemiological research.

Objective: This study aims to analyze posts on X concerning fibromyalgia, headache, paraplegia, neuropathy, and multiple sclerosis, as well as characterize the profile of users involved in these conversations, identify prevalent topics, measure public perception, evaluate treatment efficacy, and detect discussions related to the most frequent nonmedical issues.

Methods: A total of 72,874 tweets in English and Spanish containing the selected keywords were collected between 2018 and 2022. A manual review of 2500 tweets was conducted, and the larger subset was automatically classified using natural language processing methods based on the BERTweet model, previously fine-tuned for content analysis on social media platforms. Subsequently, tweets related to chronic pain conditions were analyzed to examine user types, disease origin, and both medical and nonmedical content.

Results: Of the total tweets collected, 55,451 (76.1%) were classifiable. The most active users were health care professionals and institutions. The primary perceived etiology was pharmacological, and higher treatment efficacy was noted in neuropathy, paraplegia, and multiple sclerosis. Regarding nonmedical content, there were more tweets related to the definition and understanding of the disease.

Conclusions: Social media platforms, such as X, are playing a crucial role in the dissemination of information on chronic pain. Discussions largely focus on the available treatments and the need to enhance public education, using these platforms to correct misconceptions and provide better support to patients.

In recent years, artificial intelligence (AI) has seen rapid advancements, with innovations such as large language models and generative AI evolving at a rapid pace. While this progress offers tremendous opportunities, it also presents risks, particularly in the creation, consumption, and amplification of information and its impact on population health and health program delivery. Thoughtful approaches are necessary to navigate the consequences of advances in AI for different health care professionals and patient populations and from a policy and governance perspective. Through a collaboration between the World Federation of Public Health Associations working groups, this Viewpoint article brings together perspectives, concerns, and aspirations from young adult professionals across 5 continents and from diverse backgrounds to explore the future of public health and AI in the context of the changing health information environment. Our discussion is divided into 2 parts, specifically examining aspects of disinformation and AI, and also the role of public health and medical professionals in a growing AI-driven health information ecosystem. This Viewpoint concludes with 5 key recommendations on how to potentially address issues such as information and disinformation overload; misinformation propagation; and resultant changes in health practices, research, ethics, and the need for robust policies that can dynamically address current and future challenges.

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.