PolitiSky24: U.S. Political Bluesky Dataset with User Stance Labels
This provides a timely, open-data resource for political stance analysis, addressing a gap in user-level perspectives on Bluesky, though it is incremental as it applies existing methods to a new platform.
The authors tackled the scarcity of user-level stance detection datasets for emerging platforms like Bluesky by creating PolitiSky24, the first dataset for the 2024 U.S. presidential election, which includes 16,044 user-target stance pairs with 81% labeling accuracy using large language models.
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