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

arXiv: https://arxiv.org/abs/2506.07606 · HTML: https://mlsanity.com/paper/2506.07606/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 9 Jun 2025.

## Builds on (most cited first, 13 of 13)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations](https://mlsanity.com/paper/2010.03640/) (2020)
- [Stance and Sentiment in Tweets](https://mlsanity.com/paper/1605.01655/) (2016)
- [Will-They-Won't-They: A Very Large Dataset for Stance Detection on Twitter](https://mlsanity.com/paper/2005.00388/) (2020)
- [Unsupervised User Stance Detection on Twitter](https://mlsanity.com/paper/1904.02000/) (2019)
- [KaLM-Embedding: Superior Training Data Brings A Stronger Embedding Model](https://mlsanity.com/paper/2501.01028/) (2025)
- ["I'm in the Bluesky Tonight": Insights from a Year Worth of Social Data](https://mlsanity.com/paper/2404.18984/) (2024)
- [Bluesky and the AT Protocol: Usable Decentralized Social Media](https://mlsanity.com/paper/2402.03239/) (2024)
- [A Survey of Stance Detection on Social Media: New Directions and Perspectives](https://mlsanity.com/paper/2409.15690/) (2024)
- [Bluesky: Network Topology, Polarization, and Algorithmic Curation](https://mlsanity.com/paper/2405.17571/) (2024)
- [Looking AT the Blue Skies of Bluesky](https://mlsanity.com/paper/2408.12449/) (2024)
- [BlueTempNet: A Temporal Multi-network Dataset of Social Interactions in Bluesky Social](https://mlsanity.com/paper/2407.17451/) (2024)
- [FarExStance: Explainable Stance Detection for Farsi](https://mlsanity.com/paper/2412.14008/) (2024)

## Built on by (most cited first, 2 of 2)

- [Quantifying Political Partisanship for Cross-Platform Analyses](https://mlsanity.com/paper/2607.21842/) (2026)
- [Agent-based Modeling: Equilibrium, Echo Chambers, and Efficiency in Hybrid Coevolutionary Opinion Games](https://mlsanity.com/paper/2609.27639/) (2026)

## Abstract (arXiv)

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