Out-of-Network Attention Dynamics on Bluesky

2026-10-01

AI Summary

This study investigates the mechanisms governing the distribution of user attention within decentralized social platforms. Leveraging large-scale interaction data from Bluesky, we employ social graph distance computation and attention path decomposition to analyze cross-network attention dynamics at an unprecedented scale. Our findings reveal that 74.5% of interactions are directed toward already-followed accounts, indicating that network proximity significantly constrains exploratory behavior. Furthermore, the conversion of distant content exploration into new social ties is highly inefficient, with a small subset of users dominating the exploration process while exhibiting substantial individual heterogeneity. Collectively, this work uncovers the structural bottlenecks inherent in attention flow across decentralized social networks.

Abstract

Personalized social media commonly relies on explicit follow graphs to shape what content users encounter; yet how much attention crosses ties they have not formed remains largely undocumented at scale. We study this question on Bluesky, a large decentralized microblogging platform whose default feed relies on a simple, reverse-chronological content recommender. We analyze 173 million user-author interactions (likes, reposts, replies, and quotes) collected from a near-complete platform dump between February and September 2023. We decompose each interaction by attention-path length (already followed, relayed by a followed account, reachable within two follow-hops, or beyond) and find that 74.5% of interactions reach the user through an account they already follow. Measured by distance in the follow graph rather than by route, 80.6% of interaction lands within two follow hops, far beyond the 22.4% an expected-degree null predicts. We then characterize how exploration varies across users and over tenure. A broad-reaching minority generates three quarters of all exploratory activity, while aggregate declines in exploration with tenure mask three distinct individual trajectories. Finally, attention reaching beyond two hops converts into new follow ties at less than one third the rate of two-hop-local exploratory attention. Together, these results depict a platform where out-of-network exploration is substantial in volume but strongly constrained by network proximity and unlikely to translate into new social ties.

Problem

• out-of-network attention
• social media dynamics
• follow graph
• exploratory behavior
• Bluesky

Innovation

• out-of-network attention
• attention-path decomposition
• decentralized social media
• null model
• exploration trajectories