发表机构
School of Artificial Intelligence, Shenzhen Technology University; School of Cyber Science and Technology, University of Science and Technology of China（深圳技术大学人工智能学院; 中国科学技术大学网络空间安全学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对TikTok政治对话中多目标立场分析，提出多模态分层数据集TikStance，涵盖2024年美国选举周期三位政治人物，结合多目标覆盖、分层对话与人工注释，支持多模态立场检测等多领域研究。
AI 中文摘要
政治话语日益转向短视频平台，但对此类内容的计算分析仍受限于缺乏能同时保留视听信息和分层对话的数据集。本文提出TikStance，这是一个多模态且情境感知的数据集，包含来自TikTok的161个视频和13876条评论，用于政治讨论中的立场检测。该数据集涵盖2024年美国选举周期的三位主要政治人物，内容在2023年9月至2025年1月收集。每个讨论单元将一个主视频及其元数据链接到一个父链接评论树，以便在视听和对话情境中进行立场分析。每个项目由三名注释者独立标注，最终特朗普、拜登和哈里斯子集的Krippendorff's α分别达到0.743、0.723和0.722。描述性分析进一步揭示了立场分布和对话深度中与目标相关的差异，嵌套回复占所有评论的23.3%。通过结合多目标覆盖、分层对话和可靠的多层次人工注释，TikStance支持多模态立场检测、政治传播、计算社会科学和情境感知自然语言处理等方面的研究。
英文摘要
Political discourse has increasingly moved to short-video platforms, yet computational analysis of such content remains constrained by the scarcity of datasets that jointly preserve audiovisual information and hierarchical conversations. Here we present TikStance, a multimodal and context-aware dataset comprising 161 videos and 13,876 comments from TikTok, designed for stance detection in political discussions. The dataset covers three major political figures in the 2024 U.S. election cycle--Donald Trump, Joe Biden, and Kamala Harris--with content collected between September 2023 and January 2025. Each discussion unit links a host video and its metadata to a parent-linked comment tree, enabling stance analysis within both audiovisual and conversational context. Each item was independently labeled by three annotators using a three-class scheme (Favor, Against, None) for video-to-target and comment-to-target stance; items with disagreement were re-annotated, and the final Krippendorff's \(α\) reached 0.743, 0.723, and 0.722 for the Trump, Biden, and Harris subsets, respectively. Descriptive analysis further reveals target-dependent differences in stance distributions and conversational depth, with nested replies accounting for 23.3\% of all comments. By combining multi-target coverage, hierarchical conversations, and reliable multi-level human annotations, TikStance supports research in multimodal stance detection, political communication, computational social science, and context-aware natural language processing.
