@inproceedings{rostami-etal-2025-politisky24,
    title = "{P}oliti{S}ky24: {U}.{S}. Political Bluesky Dataset with User Stance Labels",
    author = "Rostami, Peyman  and
      Rahimzadeh, Vahid  and
      Adibi, Ali  and
      Shakery, Azadeh",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1198/",
    doi = "10.18653/v1/2025.findings-emnlp.1198",
    pages = "21976--21993",
    ISBN = "979-8-89176-335-7",
    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."
}