分析抖音上支持与反对跨性别者社群的网络结构差异，揭示仇恨内容传播机制。
Characterizing Network Structure of Anti-Trans Actors on TikTok
- 构建跨性别情感分类体系，结合专家标注与RAG技术提升内容识别精度。
- 发现支持与反对群体间存在大量互动，表明跨性别者遭系统性针对性攻击。
- 研究结果为平台内容审核工具优化提供实证依据，适合政策制定者参考。
短视频社交平台如TikTok虽为跨性别/非二元创作者提供了可见性与社区连接，但也被右翼势力利用，用于传播针对跨性别群体的仇恨言论与宣传。本文收集了包含支持与反对跨性别内容的TikTok视频样本，构建了一个跨性别相关情感分类体系，以实现内容精准分类，并分析支持与反对社群的回复网络结构。研究采用来自跨性别/非二元群体的雇佣专家标注数据，确保标签高准确率。基于此，开发了一种融合检索增强生成（RAG）与分类体系定义的新型分类流程，将内容划分为支持、反对或中立三类。结果显示，引入分类体系显著提升了内容区分能力。网络分析表明，支持与反对内容发布者之间存在大量互动，进一步证实跨性别个体受到系统性针对。研究强调亟需更有效的平台内容监管工具。
原文摘要 · Abstract (English)
The recent proliferation of short form video social media sites such as TikTok has been effectively utilized for increased visibility, communication, and community connection amongst trans/nonbinary creators online. However, these same platforms have also been exploited by right-wing actors targeting trans/nonbinary people, enabling such anti-trans actors to efficiently spread hate speech and propaganda. Given these divergent groups, what are the differences in network structure between anti-trans and pro-trans communities on TikTok, and to what extent do they amplify the effects of anti-trans content? In this paper, we collect a sample of TikTok videos containing pro and anti-trans content, and develop a taxonomy of trans related sentiment to enable the classification of content on TikTok, and ultimately analyze the reply network structures of pro-trans and anti-trans communities. In order to accomplish this, we worked with hired expert data annotators from the trans/nonbinary community in order to generate a sample of highly accurately labeled data. From this subset, we utilized a novel classification pipeline leveraging Retrieval-Augmented Generation (RAG) with annotated examples and taxonomy definitions to classify content into pro-trans, anti-trans, or neutral categories. We find that incorporating our taxonomy and its logics into our classification engine results in improved ability to differentiate trans related content, and that Results from network analysis indicate many interactions between posters of pro-trans and anti-trans content exist, further demonstrating targeting of trans individuals, and demonstrating the need for better content moderation tools
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