e.g. mhealth
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To address the gaps described between HCPs and the general population throughout the pandemic, we analyze the emotions of a matched sample of HCPs and non-HCPs from a high-quality Twitter Panel dataset [25,26], selecting users who tweeted consistently throughout the study period from January 2019 to May 2022. The prepandemic period enables us to control for baseline differences.
J Med Internet Res 2025;27:e72521
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X (formerly known as Twitter) provides real-time insights into the thoughts, feelings, and conversations of millions of users. Natural language processing (NLP) tools are instrumental in analyzing social media content, offering deeper insights into public perception. NLP methods enable the analysis of public sentiment toward specific topics, the detection of emerging trends, and the identification of demographic groups participating in these discussions.
JMIR Form Res 2025;9:e60859
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In particular, Twitter (now rebranded as X, though “Twitter” is used in this article given its widely recognized name) has over 600 million monthly active users [5] and has gained traction across clinical subspecialties, journals, and academia for research dissemination [6-8]. Having a Twitter presence is becoming increasingly important as a means to reach target audiences and affect bibliometric markers of research impact.
JMIR Form Res 2025;9:e59481
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The number of social media users escalates globally [20], with nearly 50% of Japan’s population using Twitter (now X), a leading text-based social media platform [21]. Social media not only facilitates access to a broad range of information but also serves as a platform for public discussion, allowing individuals to express their views, opinions, and sentiments.
JMIR Infodemiology 2025;5:e69321
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Social media platforms, such as X (formerly Twitter), can foster collaboration, mentorship, and promotion of research [5,6]. However, studies examining X’s impact on existing gender gaps are limited. In this study, we aimed to analyze differences between X users and non–X users and differences in X use by gender among adult cardiologists.
This cross-sectional study was exempt from ethical approval by the Cedars-Sinai institutional review board due to the use of publicly available data.
The top 20 U.S.
JMIR Cardio 2025;9:e66308
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To achieve this, we collected and analyzed a large pool of images posted by individuals on the popular social media platform, Twitter. Twitter is one of the most diverse social media platforms in terms of user age [8]. Given that Twitter has limited contextual information due to tweet length restrictions and the increasing number of multimedia postings [9], our study uses text with image data to learn peoples’ dietary behaviors to learn about population-level dietary behavior in a more comprehensive way.
J Med Internet Res 2025;27:e51638
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Our research approach consisted of seven steps: (1) developing Twitter search terms, (2) establishing a period from which tweets would be collected, (3) using the Twitter application programming interface (API) to collect tweets within the defined search period, (4) processing tweets to enhance the accuracy of data analyses, (5) performing sentiment analysis using a Robustly Optimized BERT Pretraining Approach (Ro BERTa), (6) identifying themes among tweets through word frequency, and (7) collecting and further
J Med Internet Res 2025;27:e65087
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The Twitter application programming interface (API) for academics was used to collect tweets from January to December 2021. To analyze linguistic differences in pain-related discussions, we selected tweets from the 2 states with the highest opioid mortality rates (Florida and Ohio) and the 2 with the lowest rates (South and North Dakota) in 2021 [24].
J Med Internet Res 2025;27:e67506
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Stigma Attitudes Toward HIV/AIDS From 2011 Through 2023 in Japan: Retrospective Study in Japan
Stigma can manifest as various messages, eliciting message responses, and ultimately causing message effects, as defined by Smith’s stigma-communication model, which can be used to analyze tweets from X (formerly known as Twitter) [17]. A message refers to the intent of a tweet, such as a label intended to reinforce societal prejudices. A message response is a reaction or interaction generated by another user in relation to the initial message, such as accessing relevant social attitudes or stereotypes.
J Med Internet Res 2025;27:e69696
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One study found that about a quarter of HPV-related content on Twitter contained misinformation about the HPV vaccine. HPV vaccine misinformation posts had a higher average “like” count than pro-vaccine posts, making them more likely to be seen by broader audiences [22]. Viewing pro-HPV vaccine content on social media ironically exposes parents to antivaccine rhetoric, as pro-HPV vaccine content attracts antivaccine responses [23].
J Med Internet Res 2025;27:e54657
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