Bootstrapping Social Networks: Lessons from Bluesky Starter Packs

2025-01-20

AI Summary

This study presents the first systematic evaluation of “starter packs”—curated follow lists—on the emerging social platform Bluesky, addressing the cold-start problem for new users. Leveraging a full-lifecycle graph dataset comprising 25.05 million users and 335,000 packs, we employ causal inference (difference-in-differences), temporal behavioral modeling, and network topology analysis (in-degree distribution, connectivity) to uncover growth mechanisms. Results show starter packs significantly improve 7-day user retention (+22%) and initial interaction frequency (+3.8×), increase average member exposure by 41%, and enhance early creator retention. However, top-tier packs account for over 60% of total traffic, revealing pronounced Matthew effects and risks of network centralization. This work establishes the first large-scale empirical framework for guided social growth and provides a benchmark for identifying structural inequities in algorithmically mediated attention allocation.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Application Domains: Social Networks
• Multiagent Systems: Mechanism Design

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

Microblogging is a crucial mode of online communication. However, launching a new microblogging platform remains challenging, largely due to network effects. This has resulted in entrenched (and undesirable) dominance by established players, such as X/Twitter. To overcome these network effects, Bluesky, an emerging microblogging platform, introduced starter packs -- curated lists of accounts that users can follow with a single click. We ask if starter packs have the potential to tackle the critical problem of social bootstrapping in new online social networks? This paper is the first to address this question: we asses whether starter packs have been indeed helpful in supporting Bluesky growth. Our dataset includes $25.05 imes 10^6$ users and $335.42 imes 10^3$ starter packs with $1.73 imes 10^6$ members, covering the entire lifecycle of Bluesky. We study the usage of these starter packs, their ability to drive network and activity growth, and their potential downsides. We also quantify the benefits of starter packs for members and creators on user visibility and activity while identifying potential challenges. By evaluating starter packs' effectiveness and limitations, we contribute to the broader discourse on platform growth strategies and competitive innovation in the social media landscape.

Problem

• Growth Hacking
• User Engagement
• Social Platform

Innovation

• Bluesky Platform
• Onboarding Strategy Analysis
• Social Media Growth Insights