Digital misinformation and antimicrobial stewardship: cross-platform epidemiologic signals from a decade of online discourse (2015–2025)

Abstract Objective: To quantify antibiotic-related misinformation across major social networking platforms and examine its potential relevance to antimicrobial stewardship(AMS) and healthcare epidemiology. Design: A retrospective, Cross-platform infodemiology study (2015–2025). Setting: Public data from Twitter/X, Reddit, and Facebook were analyzed, and a temporal association with global antimicrobial-resistance measures from the World Health Organization’s Global Antimicrobial Resistance(AMR) Surveillance System. Methods: We extracted 1.8 million posts containing antibiotic-related key terms. Natural-language processing and transformer-based classification identified misinformation themes, sentiment polarity, and diffusion dynamics. Topic modeling and network analysis (Gephi) characterized echo-chamber structures. Temporal associations between misinformation intensity and antimicrobial-resistance indicators were examined using cross-correlation and Granger lead–lag analysis. Results: Misinformation comprised 19.3% of posts, spreading 2.4 times faster than factual content ( p