Background: Opioid overdose is a global public health emergency, with the United States experiencing high rates of morbidity and mortality due to prescription and illicit opioid use. Traditional public health monitoring systems often fail to provide real-time insights, limiting their capacity for early detection and intervention. Social media platforms, especially Reddit, offer a promising alternative for timely toxicovigilance due to the abundance of user-generated, real-time content.
Objective: This study aimed to explore the use of Reddit as a real-time, high-volume source for toxicovigilance and develop an automated system that can classify and analyze opioid-related social media posts to detect behavioral patterns and monitor the evolution of public discourse on opioid use.
Methods: To investigate evolving social media discourse around opioid use, we collected a large-scale dataset from Reddit spanning 6 years, from January 1, 2018, to December 30, 2023. Using a comprehensive opioid lexicon-including formal drug names, street slang, common misspellings, and abbreviations-we filtered relevant posts for further analysis. A subset of these data was manually annotated according to well-defined annotation guidelines into 4 categories: self-misuse, external misuse, information, and unrelated, with distributions of 37.21%, 27.25%, 27.57%, and 7.97%, respectively. To automate the classification of opioid-related chatter, we developed a robust natural language processing pipeline leveraging classical machine learning algorithms, deep learning models, and transformer-based architecture, and fine-tuned a state-of-the-art large language model (LLM; OpenAI GPT-3.5 Turbo). In the final stage, the trained LLM was deployed on an unlabeled dataset comprising 74,975 additional Reddit chatter posts. This enabled a detailed temporal analysis of opioid-related discussions, aligned with 6 years of opioid-related death records from the Centers for Disease Control and Prevention (CDC). For this study, self-misuse and external misuse were merged into a misuse category for direct comparison with the CDC's mortality data, examining whether trends in social media discourse on opioid misuse reflect patterns in real-world mortality statistics.
Results: The fine-tuned GPT-3.5 Turbo model achieved the highest classification accuracy of 0.93, outperforming the baseline (random forest 0.85) by representing a performance improvement of 9.14% over the machine learning model. The temporal analysis of the unlabeled data revealed evolving trends in opioid-related discussions, indicating shifts in user behavior and overdose-related chatter over time. To quantify this relationship, we calculated the Pearson correlation coefficient between misuse-related posts and CDC death records (r=0.854). This correlation was statistically significant (P<.001), indicating a strong positive relationship betwe
Background: The quality of health information on social media is a major concern, especially during the early stages of public health crises. While the quality of the results of the popular search engines related to particular diseases has been analyzed in the literature, the quality of health-related information on social media, such as X (formerly Twitter), during the early stages of a public health crisis has not been addressed.
Objective: This study aims to evaluate the quality of health-related information on social media during the early stages of a public health crisis.
Methods: A cross-sectional analysis was conducted on health-related tweets in the early stages of the most recent public health crisis (the COVID-19 pandemic). The study analyzed the top 100 websites that were most frequently retweeted in the early stages of the crisis, categorizing them by content type, website affiliation, and exclusivity. Quality and reliability were assessed using the DISCERN and JAMA (Journal of the American Medical Association) benchmarks.
Results: Our analyses showed that 95% (95/100) of the websites met only 2 of the 4 JAMA quality criteria. DISCERN scores revealed that 81% (81/100) of the websites were evaluated as low scores, and only 11% (11/100) of the websites were evaluated as high scores. The analysis revealed significant disparities in the quality and reliability of health information across different website affiliations, content types, and exclusivity.
Conclusions: This study highlights a significant issue with the quality, reliability, and transparency of online health-related information during a public health challenge. The extensive shortcomings observed across frequently shared websites on Twitter highlight the critical need for continuous evaluation and improvement of online health content during the early stages of future health crises. Without consistent oversight and improvement, we risk repeating the same shortcomings in future, potentially more challenging situations.
Background: The analysis of social networks should be considered by institutions and governments alongside surveys and other conventional methods for assessing public attitudes toward vaccines. X (formerly known as Twitter) has emerged as a significant source for studying vaccine hesitancy.
Objective: The aim of the study is to examine the main arguments and narratives in favor and against vaccination expressed in Spanish- and Catalan-language posts, comments, and opinions on the social media platform X.
Methods: Spanish and Catalan posts were collected from X using NodeXL Pro between March and December 2021, resulting in 479,734 posts. For qualitative analysis, a random subsample of 384 tweets was selected using Cochran's formula (95% confidence and ±5% margin of error). A bespoke code frame was developed in collaboration with medical and social media experts, and posts were translated into English. Intercoder reliability, assessed on 20% of the sample, yielded 93.4% agreement and a Cohen κ of 0.92.
Results: A total of 479,734 posts were retrieved from 29,706 users. After an inductive review of the data, six themes were identified, which formed the basis of our code frame: (theme 1) vaccine acquisition and distribution, (theme 2) vaccine skepticism and criticism, (theme 3) provaccination stance, (theme 4) global COVID-19 situation, (theme 5) vaccine politics and international relations, and (theme 6) miscellaneous news and posts. Vaccine skepticism and criticism was the most frequent theme (93/384, 24.2%), whereas vaccine politics and international relations was the least (25/384, 6.5%). We observed that while some posts supported vaccination, others expressed concerns about vaccine safety and efficacy, promoted conspiracy theories, disseminated misinformation, or opposed scientific consensus. Challenges related to vaccine acquisition and distribution within specific countries were also identified, along with political and economic factors, such as the politicization of vaccines, which hindered equitable distribution between vaccine-producing and vaccine-needing countries. Additionally, the pandemic's social impact fostered community support initiatives and solidarity.
Conclusions: Our findings can inform measures to promote vaccine acceptance and reinforce trust in health care systems, professionals, and scientific perspectives, thereby improving vaccination coverage. These insights may serve as a foundation for developing sociopolitical strategies to enhance vaccination management and address future pandemics or new vaccination campaigns.
Background: Hand, foot, and mouth disease (HFMD) is a global health concern requiring a risk assessment framework based on systematic factors analysis for prevention and control.
Objective: This study aims to construct a comprehensive HFMD risk assessment framework by integrating multisource data, including historical incidence information, environmental parameters, and web-based search behavior data, to improve predictive performance.
Methods: We integrated multisource data (HFMD cases, meteorology, air pollution, Baidu Index, and public health measures) from Bao'an District of Shenzhen city in Southern China (2014-2023). Correlation analysis was used to assess the associations between HFMD incidence and systematic factors. The impacts of environmental factors were analyzed using the Distributed Lag Nonlinear Model. Seasonal Autoregressive Integrated Moving Average model and advanced machine learning methods were used to predict HFMD 1-4 weeks ahead. Risk levels for the 1- to 4-week-ahead forecasts were determined by comparing the predicted weekly incidence against predefined thresholds.
Results: From 2014 to 2023, Bao'an District reported a total of 118,826 cases of HFMD. Environmental and search behavior factors (excluding sulfur dioxide) were significantly associated with HFMD incidence in nonlinear patterns. For 1-week-ahead prediction, Seasonal Autoregressive Integrated Moving Average using case data alone performed best (R²=0.95, r=0.98, mean absolute error=53.34, and root-mean-square error=99.31). For 2- to 4-week-ahead forecasting, machine learning models incorporating web-based and environmental data showed superior performance (R²=0.83, 0.75, and 0.64; r=0.92, 0.87, and 0.80; mean absolute error=87.84, 112.41, and 132.47; and root-mean-square error=185.08, 229.13, and 276.81). The predicted HFMD risk levels matched the observed levels with accuracies of 96%, 87%, 88%, and 83%, respectively.
Conclusions: The epidemic dynamics of HFMD are influenced by multiple factors in a nonlinear manner. Integrating multisource data, particularly web-based search behavior, significantly enhances the accuracy of short- and midterm forecasts and risk assessment. This approach offers practical insights for developing digital surveillance and early warning systems in public health.
Background: Cannabis is the third most consumed drug worldwide, with its use linked to a high number of substance use disorders, particularly among young men. Associated mortality causes include traffic accidents and cardiovascular diseases. The global expansion of cannabis legalization has sparked debates about its impact on risk perception, with risk perception decreasing in countries with permissive laws. Social media analysis, such as on Twitter (subsequently rebranded as X), is a useful tool for studying these perceptions and their variation by geographic region.
Objective: This study aims to analyze Twitter users' perceptions of cannabis use and legalization, taking into account the geographic location of the tweets.
Methods: A mixed methods approach was used to analyze cannabis-related tweets on Twitter, using keywords such as "cannabis," "marijuana," and "hashish." Tweets were collected from January 1, 2018, to April 30, 2022, in English and Spanish, and only those with at least 10 retweets were included. The content analysis involved an inductive-deductive approach, resulting in the classification of tweets into thematic categories, including discussions on legalization.
Results: The tweet analysis showed that in America, Europe, and Asia, political discussions about cannabis were the most common topic, while personal testimonies dominated in Oceania and Africa. In all continents, personal experiences with cannabis use were mostly positive, with Oceania recording the highest percentage (1642/2695, 60.93%). Regarding legalization, Oceania also led with the highest percentage of tweets in favor (1836/2695, 68.13%), followed by America and Africa, while support in Europe and Asia was slightly lower, with about half of the tweets in favor.
Conclusions: The political debate has been the most frequently mentioned topic, reflecting the current situation in which legislative changes are being discussed in many countries. The predominance of opinions in favor of legalization, combined with the prevalence of positive experiences expressed about cannabis, suggests that the health risks associated with cannabis use are being underestimated in the public debate.
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
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