P oliti S ky24: U . S . Political Bluesky Dataset with User Stance Labels (2025)

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.

## introduction stance detection is the task of automatically determining the viewpoint expressed in a text toward a specific target, such as an entity, topic, or claim @xcite . this task serves as a vital component of many nlp tasks, such as fake news detection, fact-checking, rumor verification and user profiling @xcite . furthermore, it supports applications like identifying public opinion, tracking sentiment toward politicians or products, and understanding user attitudes in social media conversations @xcite . research in stance detection spans multiple domains, each with its unique challenges and characteristics. in the financial domain, datasets like wt-wt @xcite have focused on mergers and acquisitions between major companies. health-related datasets emerged during the covid-19 pandemic, capturing positions toward public health figures and policies @xcite . general-purpose datasets such as vast @xcite and ez-stance @xcite have addressed broader topics spanning education, entertainment, and environmental protection. however, the political domain, particularly u.s. politics, has attracted the most attention due to its societal impact and complexity. numerous datasets have been developed focusing on political figures and events, with a significant emphasis on the 2016 and 2020 u.s. presidential elections @xcite @xcite . to the best of our knowledge, no datasets currently exist for the 2024 election. twitter (now x) has traditionally been the primary source for stance detection datasets, but recent api restrictions have severely limited access to public data, hampering research efforts. additionally, most existing datasets focus on individual post-level analysis, overlooking valuable contextual information from users' complete posting histories. examining stance at the user-level provides a more comprehensive understanding of political viewpoints by considering broader expression patterns rather than isolated posts. furthermore, while alternative platforms like bluesky offer decentralized architectures and greater data accessibility, they remain largely unexplored for stance detection dataset creation. to address these limitations, we present a comprehensive stance dataset focused on the 2024 us presidential candidates, specifically targeting kamala harris and donald trump. our contribution includes 16,044 user-target stance pairs with rich metadata and network information (see figure 1 ). the dataset was created through a meticulously configured pipeline that first filters users with a hashtag-based algorithm to identify relevant accounts, then employs llms to determine stance based on high-quality chunks of user history. we developed carefully crafted validation datasets to evaluate different components, testing various embedding models for context retrieval and llms for stance labeling. our thoroughly evaluated pipeline provides stance labels with reasoning explanations, relevant text spans, and complete network structures of users' interactions, namely repost, like and following. the dataset enables analysis of political discourse on emerging platforms while offering insights into llm capabilities for understanding complex political communication. the remainder of this paper is organized as follows: section 2 reviews related work in both stance detection and studies of the bluesky platform. section 3 describes our bluesky dataset, detailing the network structure and data collection methodologies that resulted in our us political dataset. section 4 presents our stance labeling pipeline and the resulting stance dataset. section 5 provides experimental results and comprehensive error analysis of llm performance in stance detection and presents key insights derived from our dataset. section 6 describes future outlooks in detail. finally, section 7 concludes the paper and discusses future research directions. despite these advances, existing work has mainly focused on post-level (e.g., tweet) stance detection on centralized platforms, with limited attention to user-level stance detection on decentralized networks like bluesky. ## stance detection after collecting the bluesky dataset related to u.s. politics in the previous section, we now focus on annotating the stances of the bluesky users toward the key figures in the 2024 u.s. election. in this section, we detail the annotation process. ## analysis this section presents a performance analysis of our stance labeling pipeline's components (figure 3(b) ) and error patterns. we conclude by examining the stance distribution characteristics on the final dataset. ## future outlook and broader impact in an era where declining data accessibility from major social platforms has created significant obstacles for researchers, our work offers both a timely resource and a replicable methodological blueprint. the public release of politisky24 provides a foundational, open-data cornerstone intended to catalyze a new wave of transparent and verifiable research on emerging platforms like bluesky. be-yond the dataset itself, the robust pipeline developed for its creation serves as an adaptable framework for the community, demonstrating a clear path forward for creating high-quality, labeled datasets from other open platforms. the dataset's primary value lies in the new interdisciplinary research it enables. while annotated for stance, politisky24 is designed as a foundational layer that can be extended to investigate a wide range of complex social and political phenomena. researchers can introduce new annotations to study the intricate interplay between a user's political stance and their online behaviors, such as the dissemination of misinformation, the use of hyper-partisan language, or engagement with hate speech. furthermore, the dataset invites a powerful integration of its stance labels with its rich network and temporal data. by analyzing the network topology, researchers can map the structure of political echo chambers, identify influential actors who drive conversations, and model the diffusion of specific narratives through the platform. the longitudinal nature of the data also allows for dynamic, event-driven analysis, enabling studies that track how collective opinion shifts in response to major real-world events, such as candidate debates, policy announcements, or breaking news. ## conclusion in this study, we constructed a labeled dataset capturing user stances on the bluesky social network toward the main 2024 u.s. presidential candidates: trump and harris. labels were assigned using a pipeline that combined text embedding models for context retrieval with a large language model (llm) for stance detection, achieving an overall accuracy of 81%. among the 19% of misclassifications, roughly one-third stemmed from context + llm retriever errors, while the remaining twothirds were attributed to challenges in language understanding and interpretation. among the models tested, the kalm-mini-v1.5 embedding model and deepseek-v3 llm outperformed other stateof-the-art alternatives in this pipeline. for future work, we aim to leverage users' graph networks to assist with data labeling. in addition, as a separate direction, we plan to apply our pipeline across different time windows to examine how users' stances evolve over time.