PolitiSky24: U.S. Political Bluesky Dataset with User Stance Labels

TL;DR

PolitiSky24 is a novel, large-scale dataset for U.S. political stance detection on Bluesky, combining user histories, engagement data, and LLM-generated labels to facilitate holistic political analysis.

Contribution

It introduces the first user-level stance detection dataset for the 2024 U.S. election, created with an innovative pipeline using LLMs for transparent labeling.

Findings

Achieves 81% labeling accuracy with LLMs.

Provides comprehensive user posting histories and engagement metadata.

Enables holistic political stance analysis on emerging social platforms.

Abstract

Stance detection identifies the viewpoint expressed in text toward a specific target, such as a political figure. While previous datasets have focused primarily on tweet-level stances from established platforms, user-level stance resources, especially on emerging platforms like Bluesky remain scarce. User-level stance detection provides a more holistic view by considering a user's complete posting history rather than isolated posts. We present the first stance detection dataset for the 2024 U.S. presidential election, collected from Bluesky and centered on Kamala Harris and Donald Trump. The dataset comprises 16,044 user-target stance pairs enriched with engagement metadata, interaction graphs, and user posting histories. PolitiSky24 was created using a carefully evaluated pipeline combining advanced information retrieval and large language models, which generates stance labels with supporting rationales and text spans for transparency. The labeling approach achieves 81\% accuracy with scalable LLMs. This resource addresses gaps in political stance analysis through its timeliness, open-data nature, and user-level perspective. The dataset is available at https://doi.org/10.5281/zenodo.15616911