Paper Overview
Field: NLP Authors: Yazhi Zhang, Fuqiang Niu, Bowen Zhang Published: 2025-07-16 arXiv: 2507.12492
Original Abstract
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 relabeled, and final Krippendorff's alpha for the Trump, Biden, and Harris subsets reached 0.743, 0.723, and 0.722 respectively. Descriptive analysis further reveals target-dependent differences in stance distribution 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 annotation, TikStance supports research on multimodal stance detection, political communication, computational social science, and context-aware natural language processing.
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