JMIR Infodemiology

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.

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.

Following the signing of the Tobacco 21 Amendment (T21) in December 2019 to raise the minimum legal age for the sale of tobacco products from 18 to 21 years in the United States, there is a need to monitor public responses and potential unintended consequences. Social media platforms, such as Twitter (subsequently rebranded as X), can provide rich data on public perceptions.

The growing availability of big data spontaneously generated by social media platforms allows us to leverage natural language processing (NLP) methods as valuable tools to understand the opioid crisis.

The COVID-19 pandemic has had a significant impact on different countries because of which various health and safety measures were implemented, with digital media playing a pivotal role. However, digital media also pose significant concerns such as misinformation and lack of direction.

During health emergencies, effective infodemic management has become a paramount challenge. A new era marked by a rapidly changing information ecosystem, combined with the widespread dissemination of misinformation and disinformation, has magnified the complexity of the issue. For infodemic management measures to be effective, acceptable, and trustworthy, a robust framework of ethical considerations is needed.

Manually analyzing public health–related content from social media provides valuable insights into the beliefs, attitudes, and behaviors of individuals, shedding light on trends and patterns that can inform public understanding, policy decisions, targeted interventions, and communication strategies. Unfortunately, the time and effort needed from well-trained human subject matter experts makes extensive manual social media listening unfeasible. Generative large language models (LLMs) can potentially summarize and interpret large amounts of text, but it is unclear to what extent LLMs can glean subtle health-related meanings in large sets of social media posts and reasonably report health-related themes.

Politicization and misinformation or disinformation of unproven COVID-19 therapies have resulted in communication challenges in presenting science to the public, especially in times of heightened public trepidation and uncertainty.

Infectious disease surveillance is difficult in many low- and middle-income countries. Information market (IM)–based participatory surveillance is a crowdsourcing method that encourages individuals to actively report health symptoms and observed trends by trading web-based virtual “stocks” with payoffs tied to a future event.

TikTok (ByteDance) experienced a surge in popularity during the COVID-19 pandemic as a way for people to interact with others, share experiences and thoughts related to the pandemic, and cope with ongoing mental health challenges. However, few studies have explored how youth use TikTok to learn about mental health.

Prenatal alcohol exposure represents a substantial public health concern as it may lead to detrimental outcomes, including pregnancy complications and fetal alcohol spectrum disorder. Although UK national guidance recommends abstaining from alcohol if pregnant or planning a pregnancy, evidence suggests that confusion remains on this topic among members of the public, and little is known about what questions people have about consumption of alcohol in pregnancy outside of health care settings.

TikTok is one of the most-used and fastest-growing social media platforms in the world, and recent reports indicate that it has become an increasingly popular source of news and information in the United States. These trends have important implications for public health because an abundance of health information exists on the platform. Women are among the largest group of TikTok users in the United States and may be especially affected by the dissemination of health information on TikTok. Prior research has shown that women are not only more likely to look for information on the internet but are also more likely to have their health-related behaviors and perceptions affected by their involvement with social media.