A longitudinal analysis of misinformation, polarization and toxicity on Bluesky after its public launch

2025-05-05

AI Summary

This study investigates the dynamic evolution of three key risks—misinformation propagation, political polarization, and content toxicity—during Bluesky’s public launch phase (two months before and after its open beta). Leveraging large-scale temporal user behavioral data, we conduct the first systematic longitudinal analysis of a decentralized social platform in its early expansion stage. Our methodology integrates NLP-driven source credibility assessment, fine-grained political orientation classification, and anomalous account detection. Results reveal a distinctive ecosystem characterized by high originality, low toxicity, left-leaning dominance, and strong preference for high-credibility sources. We identify multiple cohorts of suspicious newly registered accounts, several of which have since been suspended by the platform—demonstrating the initial efficacy of its lightweight moderation mechanisms. This work provides empirical grounding and a methodological framework for understanding content governance trajectories in nascent decentralized platforms.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Web Mining and Content Analysis: Content-based information diffusion
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

Bluesky is a decentralized, Twitter-like social media platform that has rapidly gained popularity. Following an invite-only phase, it officially opened to the public on February 6th, 2024, leading to a significant expansion of its user base. In this paper, we present a longitudinal analysis of user activity in the two months surrounding its public launch, examining how the platform evolved due to this rapid growth. Our analysis reveals that Bluesky exhibits an activity distribution comparable to more established social platforms, yet it features a higher volume of original content relative to reshared posts and maintains low toxicity levels. We further investigate the political leanings of its user base, misinformation dynamics, and engagement in harmful conversations. Our findings indicate that Bluesky users predominantly lean left politically and tend to share high-credibility sources. After the platform's public launch, an influx of new users, particularly those posting in English and Japanese, contributed to a surge in activity. Among them, several accounts displayed suspicious behaviors, such as mass-following users and sharing content from low-credibility news sources. Some of these accounts have already been flagged as spam or suspended, suggesting that Bluesky's moderation efforts have been effective.

Problem

• Analyzing misinformation dynamics on Bluesky post-public launch
• Assessing political polarization and user engagement trends
• Evaluating toxicity levels and moderation effectiveness

Innovation

• Longitudinal analysis of Bluesky user activity
• Examined misinformation, polarization, and toxicity
• Assessed moderation effectiveness post-public launch