[论文] TikStance: A Multimodal and Hierarchical Dataset for Multi-target Stance Analysis in TikTok Political Conversations

论文概要

研究领域: cs.CL 作者: Yazhi Zhang, Fuqiang Niu, Bowen Zhang 发布时间: 2026-07-16 arXiv: 2607.15240

中文摘要

政治话语已日益转向短视频平台，但对这类内容的计算分析仍受限于缺乏同时保留视听信息和层级对话结构的数据集。本文提出 TikStance，一个多模态且上下文感知的数据集，包含来自 TikTok 的 161 个视频和 13,876 条评论，专为政治讨论中的立场检测设计。数据集覆盖 2024 年美国大选周期中三位主要政治人物——Donald Trump、Joe Biden 和 Kamala Harris——内容收集时间为 2023 年 9 月至 2025 年 1 月。每个讨论单元将主视频及其元数据与父子链接的评论树关联，支持在视听和对话上下文内进行立场分析。每个项目由三位标注员独立使用三分类方案（支持、反对、无）标注视频对目标和评论对目标的立场；存在分歧的项目重新标注，最终 Trump、Biden 和 Harris 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存在目标依赖差异，嵌套回复占所有评论的 23.3%。通过结合多目标覆盖、层级对话和可靠的多层人工标注，TikStance 支持多模态立场检测、政治传播、计算社会科学和上下文感知自然语言处理等领域的研究。

原文摘要

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.

--- *自动采集于 2026-07-20*

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