"I'm in the Bluesky Tonight": Insights from a Year Worth of Social Data

TL;DR

This paper introduces a comprehensive dataset from Bluesky Social, capturing user interactions, content, and algorithm outputs, enabling advanced analysis of online social behavior and misinformation spread.

Contribution

The authors release a large, high-coverage social media dataset from Bluesky, including posts, interactions, and algorithm outputs, addressing data scarcity issues in computational social science.

Findings

Enables analysis of online behavior and engagement patterns

Provides ground-truth data for studying content virality

Facilitates research on misinformation and content diffusion

Abstract

Pollution of online social spaces caused by rampaging d/misinformation is a growing societal concern. However, recent decisions to reduce access to social media APIs are causing a shortage of publicly available, recent, social media data, thus hindering the advancement of computational social science as a whole. We present a large, high-coverage dataset of social interactions and user-generated content from Bluesky Social to address this pressing issue. The dataset contains the complete post history of over 4M users (81% of all registered accounts), totalling 235M posts. We also make available social data covering follow, comment, repost, and quote interactions. Since Bluesky allows users to create and bookmark feed generators (i.e., content recommendation algorithms), we also release the full output of several popular algorithms available on the platform, along with their timestamped ``like'' interactions and time of bookmarking. This dataset allows unprecedented analysis of online behavior and human-machine engagement patterns. Notably, it provides ground-truth data for studying the effects of content exposure and self-selection and performing content virality and diffusion analysis.