![[2602.14406] TruthStance: An Annotated Dataset of Conversations on Truth Social](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.14406] TruthStance: An Annotated Dataset of Conversations on Truth Social
Summary
TruthStance introduces a comprehensive dataset of conversations from Truth Social, focusing on argument mining and stance detection, with human-annotated benchmarks for evaluating language models.
Why It Matters
This dataset addresses a gap in research on alt-tech platforms, providing valuable insights into online discourse dynamics. It enables researchers to analyze argumentation patterns and enhances the understanding of opinion formation in less-studied environments.
Key Takeaways
- TruthStance consists of 24,378 posts and 523,360 comments from Truth Social.
- The dataset includes a human-annotated benchmark for argument mining and stance detection.
- It enables analysis of argumentation patterns across various dimensions such as depth and topics.
- The study highlights the importance of understanding discourse on alternative social media platforms.
- All data and code are publicly available for further research.
Computer Science > Computation and Language arXiv:2602.14406 (cs) [Submitted on 16 Feb 2026] Title:TruthStance: An Annotated Dataset of Conversations on Truth Social Authors:Fathima Ameen, Danielle Brown, Manusha Malgareddy, Amanul Haque View a PDF of the paper titled TruthStance: An Annotated Dataset of Conversations on Truth Social, by Fathima Ameen and 3 other authors View PDF HTML (experimental) Abstract:Argument mining and stance detection are central to understanding how opinions are formed and contested in online discourse. However, most publicly available resources focus on mainstream platforms such as Twitter and Reddit, leaving conversational structure on alt-tech platforms comparatively under-studied. We introduce TruthStance, a large-scale dataset of Truth Social conversation threads spanning 2023-2025, consisting of 24,378 posts and 523,360 comments with reply-tree structure preserved. We provide a human-annotated benchmark of 1,500 instances across argument mining and claim-based stance detection, including inter-annotator agreement, and use it to evaluate large language model (LLM) prompting strategies. Using the best-performing configuration, we release additional LLM-generated labels for 24,352 posts (argument presence) and 107,873 comments (stance to parent), enabling analysis of stance and argumentation patterns across depth, topics, and users. All code and data are released publicly. Subjects: Computation and Language (cs.CL); Artificial Intelligence (c...
