TikStance: A Multimodal and Hierarchical Dataset for Multi-target Stance Analysis in TikTok Political Conversations
Score
79首次提供短视频平台多目标、层次化对话的多模态立场数据，填补了领域空白。
标注流程严谨，一致性高，但视频数量偏少，评论分布可能不均衡。
可直接用于训练多模态立场检测模型，对计算政治学和社交媒体研究实用性强。
聚焦2024美国大选周期，数据截止2025年1月，具有极强的时效性。
来自arXiv预印本，标注方法透明，代码和数据集预计公开，可信度高。
Base 87.5 × T2 Source weight ×0.9 = 79
Curation line 70Selected
Why selected
Moderate size but high annotation quality and unique hierarchical structure, enabling fine-grained context-aware stance analysis.
AI commentary
「TikStance's design cleverly combines hierarchical comment trees with host videos, capturing the unique 'reply chain' dynamics of short-video platforms that traditional text datasets cannot offer. For researchers and practitioners in social media analytics, training models directly on real TikTok scenarios has high practical value.」
Quick overview
TikStance is a multimodal and hierarchical dataset for stance analysis in political conversations on TikTok, comprising 161 videos and 13,876 comments covering three major U.S. political figures in the 2024 election cycle: Donald Trump, Joe Biden, and Kamala Harris. Each discussion unit links a host video to a parent-linked comment tree, enabling stance analysis within both audiovisual and conversational context. The dataset was annotated by three independent annotators using a three-class scheme (Favor, Against, None), achieving Krippendorff's α of 0.743, 0.723, and 0.722 for the three subsets. Nested replies account for 23.3% of all comments. TikStance supports research in multimodal stance detection, political communication, computational social science, and context-aware NLP.
- Covers 161 videos and 13,876 comments on three 2024 U.S. election candidates
- Multimodal (audiovisual + text) with hierarchical comment trees
- High inter-annotator agreement after re-annotation (α≥0.72)
- 23.3% nested replies enable context-aware stance analysis
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