MurkySky: Analyzing News Reliability on Bluesky
Vikas Reddy, Giovanni Luca Ciampaglia

TL;DR
This paper presents MurkySky, a comprehensive analysis of news reliability on Bluesky, revealing that reliable news is prevalent but unreliable sources are more partisan and topic-specific, with a left-leaning bias.
Contribution
It introduces MurkySky, a new tool for tracking unreliable news sources on Bluesky, and provides the first detailed analysis of news reliability and source bias on the platform.
Findings
Reliable news is prevalent on Bluesky.
Unreliable sources are more partisan and left-leaning.
Unreliable content concentrates on specific topics.
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…
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Taxonomy
TopicsComputational and Text Analysis Methods
