Accessing clinical trial information via two social media platforms: the new normal for patients and stakeholders

Abstract Social media platforms play an increasingly important role in the dissemination of clinical trial information and in shaping public perceptions of medical research, with direct implications for patient recruitment to clinical trials, informed decision-making, clinician–patient communication, and pharmacovigilance. Yet platform-specific communication dynamics may contribute to fragmentation in health-related discourse and to inequitable access to trial-related information. This study aimed to compare thematic structures, user activity patterns, and emotional framing of clinical trial discussions on Bluesky and Twitter (X). Two datasets were analysed: Bluesky posts (January–May 2025, n = 8849) and Twitter (X) posts (June 25–30, 2025, n = 5520). Due to differences in data accessibility across platforms, the datasets are not temporally equivalent and are interpreted within an exploratory comparative framework. Text mining techniques included tokenisation, n-gram analysis, sentiment analysis (Bing lexicon), emotion analysis (NRC lexicon), co-occurrence networks, and Jaccard similarity. The analysis indicates that Bluesky discourse was more cohesive and research-oriented, with a relatively more specialised vocabulary. In contrast, Twitter (X) exhibited more fragmented and event-driven communication patterns, along with higher positivity and greater variability in content. The Jaccard similarity coefficient (0.159) suggests substantial lexical divergence between platforms. The findings suggest that social media platforms differ in how clinical trial information is communicated. Bluesky may function as a more specialised knowledge-sharing environment, whereas Twitter (X) reflects more event-driven and socially mediated communication dynamics. These results should be interpreted as exploratory and context-dependent, reflecting differences in platform architecture and data access conditions. The observed platform-specific patterns have direct relevance to clinical practice: they can inform clinical trial recruitment strategies, clinician–patient communication around emerging therapies, evidence-based patient education on participation in clinical research, and pharmacovigilance signal detection.