Social media and health care: a cross-sectional analysis of X (Twitter) posts using the NodeXL platform

Introduction. The landscape of healthcare and medical education is rapidly evolving, driven by the popularity of social media platforms like Facebook, WeChat, and X (Twitter). These platforms have transformed healthcare communication, enabling trainees, medical students, and practitioners to enhance learning and clinical practice. Educators leverage social media to simplify complex medical concepts, fostering continuous learning and professional development. Despite their benefits, social media, notably X (Twitter), boasting over 186 million active users, are susceptible to misinformation, underscoring the importance of verifying data accuracy and identifying influential medical sources. This study aims to identify the most influential sources of health information through X (Twitter) analysis using the NodeXL tool. Methods. Employing a cross-sectional design, this study utilized the NodeXL platform for social media analysis. NodeXL facilitated the identification of prevalent topics in public health discussions on X (Twitter). Influencers, users, and prominent healthcare providers were categorized to assess the credibility of their information. Data collection extended up to March 1, 2024. Results. Analysis revealed that the #hospital hashtag garnered the highest number of tweets among popular healthcare hashtags (6,438 tweets, with 84% being unique). Conversely, #medicalcare and #medicine had the lowest citation counts (58 and 116 respectively). @MahaHealthIE emerged as the most famous account using the #healthservices hashtag. Notable influencers with the #hospital hashtag included YouTube, Narendra Modi, and Al Jazeera English. @GreenSwelfares led in using the #medicalcare hashtag, while YouTube and the World Health Organization (@WHO) were prominent among accounts using #medicine. Conclusion. This study underscores the significance of monitoring social media and tracking key influencers periodically. However, the study's limitation lies in its snapshot nature, failing to capture the dynamic shifts in influencer popularity trends over time.