Why Academics Are Leaving Twitter for Bluesky

2025-05-30

AI Summary

This study investigates the behavioral patterns and underlying mechanisms driving the migration of 300,000 academic users from Twitter/X to Bluesky between early 2023 and 2025. Method: Integrating bibliometric analysis, cross-platform behavioral tracking, and a novel identity-matching technique, we construct a time-varying Cox proportional hazards model and an ego-level contagion classification framework. Contribution/Results: We find that information-source influence dominates over audience effects and decays exponentially—a previously undocumented pattern. Simple contagion accounts for 67% of migrations, challenging prevailing assumptions of homophily and complex contagion. Overall migration rate is 18%; network reconstruction extent on Bluesky strongly predicts subsequent platform activity. These findings provide critical empirical grounding for platform migration theory and underscore the central role of information sources in reducing user switching costs.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Web Mining and Content Analysis: Content-based information diffusion
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

We analyse the migration of 300,000 academic users from Twitter/X to Bluesky between 2023 and early 2025, combining rich bibliometric data, longitudinal social-media activity, and a novel cross-platform identity-matching pipeline. We show that 18% of scholars in our sample transitioned, with transition rates varying sharply by discipline, political expression, and Twitter engagement but not by traditional academic metrics. Using time-varying Cox models and a matched-pairs design, we isolate genuine peer influence from homophily. We uncover a striking asymmetry whereby information sources drive migration far more powerfully than audience, with this influence decaying exponentially within a week. We further develop an ego-level contagion classifier, revealing that simple contagion drives two-thirds of all exits, shock-driven bursts account for 16%, and complex contagion plays a marginal role. Finally, we show that scholars who rebuild a higher fraction of their former Twitter networks on Bluesky remain significantly more active and engaged. Our findings provide new insights onto theories of network externalities, directional influence, and platform migration, highlighting information sources' central role in overcoming switching costs.

Problem

• Analyzing academic user migration from Twitter to Bluesky
• Examining factors influencing migration rates by discipline and engagement
• Investigating peer influence and information sources in platform switching

Innovation

• Cross-platform identity-matching pipeline for user tracking
• Time-varying Cox models for peer influence analysis
• Ego-level contagion classifier for migration patterns