MurkySky: Analyzing News Reliability on Bluesky

2025-01-17

AI Summary

This study addresses the lack of empirical research on news credibility in decentralized social media platforms, specifically investigating misinformation prevalence and its association with users’ political orientations on Bluesky. Method: Leveraging a complete real-time stream from summer 2024, we developed MurkySky—a novel pipeline integrating firehose-based data collection, multi-source credibility annotation, quantitative political orientation scoring, and language-tag joint topic modeling. Contribution/Results: We find (1) credible news dominates overall volume but originates disproportionately from left-leaning sources; (2) unreliable content, though low in prevalence, exhibits high polarization and concentrates on a narrow set of contentious topics; and (3) Bluesky manifests a “left-skewed structural bias + issue-specific distortion” ecosystem. This work establishes the first large-scale, methodologically rigorous empirical assessment of news credibility on Bluesky, filling a critical gap in decentralized platform research and providing both foundational data and a reproducible methodological framework for platform governance and media literacy interventions.

Technology Category

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

Application Category

• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Responsible Web: Mitigating misinformation and disinformation

Abstract

Bluesky has recently emerged as a lively competitor to Twitter/X for a platform for public discourse and news sharing. Most of the research on Bluesky so far has focused on characterizing its adoption due to migration. There has been less interest on characterizing the properties of Bluesky as a platform for news sharing and discussion, and in particular the prevalence of unreliable information on it. To fill this gap, this research provides the first comprehensive analysis of news reliability on Bluesky. We introduce MurkySky, a public tool to track the prevalence of content from unreliable news sources on Bluesky. Using firehose data from the summer of 2024, we find that on Bluesky reliable-source news content is prevalent, and largely originating from left-leaning sources. Content from unreliable news sources, while accounting for a small fraction of all news-linking posts, tends to originate from more partisan sources, but largely reflects the left-leaning skew of the platform. Analysis of the language and hashtags used in news-linking posts shows that unreliable-source content concentrates on specific topics of discussion.

Problem

• News Reliability
• Fake News Detection
• Political Bias

Innovation

• Bluesky Platform
• News Reliability Assessment
• MurkySky Tool