构建首个面向TikTok政治讨论的多模态层次化立场数据集
TikStance: A Multimodal and Hierarchical Dataset for Multi-target Stance Analysis in TikTok Political Conversations
- 整合视频、评论与层级对话结构，支持多目标立场分析
- 覆盖161个视频、1.39万条评论，三类政治人物立场标注一致率达0.72以上
- 适合研究社交媒体政治传播、多模态情感分析与上下文感知模型的人
政治话语正加速向短视频平台迁移，但现有计算分析受限于缺乏同时保留音视频信息与层级对话结构的数据集。本文提出TikStance，一个包含161个视频和13,876条评论的多模态、上下文感知数据集，用于分析2024年美国大选期间特朗普、拜登、哈里斯三位政治人物相关的立场。数据收集时间为2023年9月至2025年1月，每条讨论单元关联主视频及其元数据与父级评论树，支持音视频与对话上下文中的立场识别。每个样本由三位标注员独立标注视频/评论对目标的立场（支持、反对、无立场），分歧项重新标注，最终各子集的Krippendorff's α分别为0.743（特朗普）、0.723（拜登）、0.722（哈里斯）。描述性分析显示立场分布与对话深度存在目标差异，嵌套回复占所有评论的23.3%。TikStance通过多目标覆盖、层级对话结构与可靠的人工标注，推动多模态立场检测、政治传播、计算社会科学研究及上下文感知自然语言处理的发展。
原文摘要 · Abstract (English)
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.
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