首个面向2024美国大选的用户级立场数据集，聚焦蓝星平台用户对哈里斯与特朗普的立场。
PolitiSky24: U.S. Political Bluesky Dataset with User Stance Labels
- 基于检索与大模型结合的自动标注流程，生成带理由和文本片段的立场标签。
- 涵盖16,044个用户-目标立场对，包含互动图谱与完整发文历史。
- 适合政治分析、社交媒体研究者使用，数据开源且具时效性。
立场检测旨在识别文本中针对特定目标（如政治人物）表达的观点。以往数据集多聚焦于主流平台上的推文级立场，而新兴平台如蓝星（Bluesky）的用户级立场资源仍十分匮乏。用户级立场检测通过分析用户的全部发文历史，提供更全面的视角。本文提出首个面向2024年美国大选的蓝星平台立场数据集——PolitiSky24，聚焦卡玛拉·哈里斯与唐纳德·特朗普。数据集包含16,044个用户-目标立场对，附有互动元数据、交互图谱及用户完整发文记录。该数据集通过融合先进信息检索与大语言模型的评估化流程构建，可生成带支持理由和文本片段的立场标签，标注准确率达81%。本资源填补了政治立场分析在时效性、开放性与用户层级视角上的空白，数据已公开发布于https://doi.org/10.5281/zenodo.15616911。
原文摘要 · Abstract (English)
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
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