The Rise of Bluesky

TL;DR

The study analyzes the rapid growth and evolving network structure of Bluesky from August 2023 to February 2025 to assess whether the platform reaches a stable, self-sustaining state. It leverages four migration waves and a 1% sample of the follower network to track engagement, connectivity, and diffusion potential, introducing a network-time framework that aligns growth with user influx. Findings reveal a persistent active base, a giant follower component forming early, high clustering relative to random networks, and rising degree heterogeneity driven by influential hubs, indicating maturation toward structures seen on established platforms. The work also discusses implications for moderation and abuse as mainstream adoption unfolds, highlighting the platform’s potential yet underscoring challenges in safeguarding the ecosystem.

Abstract

This study investigates the rapid growth and evolving network structure of Bluesky from August 2023 to February 2025. Through multiple waves of user migrations, the platform has reached a stable, persistently active user base. The growth process has given rise to a dense follower network with clustering and hub features that favor viral information diffusion. These developments highlight engagement and structural similarities between Bluesky and established platforms.

Figures (1)

• Figure 1: Daily engagement and network metrics on Bluesky. Vertical dashed lines represent four major events. (a) Profile creation occurred in bursts corresponding to these events. We assign users to four groups based on the periods in which they joined (shown by the colors). (b) Total activity by user group. The dashed line corresponds to users outside of the four major groups. (c) Visualization of the follower network based on a 1% sample of the users. Panels (d-i) plot various quantities versus the increasing number of users; only 26 million users with at least one friend or follower as of 7 February 2025 are considered. Note that the number of users can occasionally decrease due to deletion and suspension of accounts. (d) Total activity by user group. (e) Average activity per active user, defined as having at least one post, repost, reply, or like. (f) Daily active users. We plot the absolute count (black) and the percentage out of total users (gray). (g) Average out-degree by user group. (h) Average clustering coefficient of the Bluesky follower network (black). We also plot the expected value in a random network with the same number of nodes and edges, which is given by the average degree divided by the node count (gray). (i) Gini (black) and Kappa (gray) indices of in-degree heterogeneity.