---
title: "TikStance：面向TikTok政治对话的多模态层次化立场分析数据集"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.15240v1"
permalink: "https://feed.astralor.com/items/itm_de350271c9c4612c541466ea"
published: "2026-07-16T17:37:19.000Z"
score: 79
channel: "ai"
tags: ["TikStance","TikTok","多模态","层次化数据","政治对话","立场检测"]
---

# TikStance：面向TikTok政治对话的多模态层次化立场分析数据集

## AI 点评

> TikStance的数据结构设计非常巧妙——层级评论树结合主视频，能够捕捉到短视频平台特有的“跟评链”效应，这是传统文本数据集无法提供的。对研究社交媒体的同学和从业者来说，直接在真实TikTok场景中训练模型，落地价值很高。

## 速览

TikStance是一个针对TikTok平台政治对话的多模态层次化数据集，包含161个视频和13,876条评论，覆盖2024年美国大选三位政治人物（特朗普、拜登、哈里斯）的立场分析。每个讨论单元包含主视频和层级评论树，由三名标注者独立标注三分类立场（支持、反对、无），最终一致性达到0.72以上。数据集中嵌套回复占全部评论的23.3%，支持多模态立场检测、政治传播和计算社会科学研究。

## 关键点

- 覆盖2024美国大选三位候选人（特朗普、拜登、哈里斯）的TikTok视频与评论
- 多模态数据（视频+文本）及层次化评论树结构
- 三独立标注者人工标注，重标后一致性高（α≥0.72）
- 嵌套回复占比23.3%，支持上下文感知的立场分析

## 全文

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.

---

引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_de350271c9c4612c541466ea