Why Academics Are Leaving Twitter for Bluesky (2505.24801v1)

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.

Analysis of Academic Migration from Twitter/X to Bluesky

The paper "Why Academics Are Leaving Twitter for Bluesky" provides a comprehensive examination of the migration patterns of academic users transitioning from Twitter/X to Bluesky between 2023 and early 2025. The authors analyze a dataset comprising 300,000 academics and utilize bibliometric data, social-media activity metrics, and a novel identity-matching pipeline to explore the dynamics of this transition. The paper uncovers various aspects influencing user migration, including discipline, political expression, and engagement, while also detailing the impact of network effects and cross-platform identity preservation.

Key Findings

The research presents several notable findings. First, the transition rate stands at 18%, revealing substantial variation across disciplines. Academics in Arts and Humanities exhibit the highest migration rates (31.3%), whereas fields like Medicine and Engineering show lower rates (13.3% and 15.7%, respectively). Political expression emerges as a significant factor; scholars with progressive views on issues like abortion rights and economic inequality are more likely to switch platforms.

The paper highlights the asymmetrical influence within social networks, emphasizing that information sources (accounts followed) exert more significant influence over users' migration decisions than audience members (followers). This phenomenon is quantified using time-varying Cox models that showcase the stronger impact of transitioned followees on departure risks compared to followers.

The authors develop an ego-level contagion classifier to decode the transition mechanisms, identifying simple contagion as the predominant driver, accounting for 66.7% of exits. Shock-driven bursts contribute to 16% of transitions, indicating how external events can trigger sudden migration waves.

Implications

From a theoretical perspective, the paper provides new insights into network externalities and platform migration theories, challenging the conventional assumption of symmetrical influence in social networks. By dissecting the migration dynamics, the authors advance the understanding of information source dominance and its implications for overcoming switching costs.

Practically, the findings have implications for platform designers aiming to foster user retention and engagement. The significant role of network preservation in sustaining activity on Bluesky suggests that platforms should facilitate seamless social network reconstruction to enhance user commitment.

Future Directions

This research opens avenues for further exploration into platform migration phenomena. Understanding how different user groups perceive switching costs and benefits could refine models of network influence. Additionally, investigating how ideological shifts in platform governance affect user behavior across diverse professional communities might yield actionable insights for social media platforms navigating user retention challenges.

The paper's comprehensive analysis of academic migration behavior offers valuable contributions to the discourse on social network dynamics, providing a granular understanding of the factors that drive platform transitions among scholars.