通过结构化推理分析健康博主话语中的隐含建议与论证关系。
TAIGR: Towards Modeling Influencer Content on Social Media via Structured, Pragmatic Inference
- 识别博主的核心推荐观点，构建论证图谱解释其理由。
- 在健康类视频语料上验证，结构化推理比逐句验证更准确。
- 适合关注社交媒体信息真实性与传播机制的研究者。
健康类博主在塑造公众认知方面作用日益重要，但其内容多以对话叙事和修辞策略表达，而非明确的事实陈述。这导致传统的以主张为中心的验证方法难以捕捉其语用意义。本文提出TAIGR（基于接地参考的建议论证推理）框架，分三步分析博主话语：(1) 提取核心推荐观点——‘建议’；(2) 构建论证图谱，揭示支持该建议的推理链条；(3) 采用因子图进行概率推理，验证建议的合理性。在健康类博主视频转录文本上的内容验证任务中，实验表明，准确验证依赖于对话语的语用与论证结构建模，而非将转录文本视为孤立主张的平铺集合。
原文摘要 · Abstract (English)
Health influencers play a growing role in shaping public beliefs, yet their content is often conveyed through conversational narratives and rhetorical strategies rather than explicit factual claims. As a result, claim-centric verification methods struggle to capture the pragmatic meaning of influencer discourse. In this paper, we propose TAIGR (Takeaway Argumentation Inference with Grounded References), a structured framework designed to analyze influencer discourse, which operates in three stages: (1) identifying the core influencer recommendation--takeaway; (2) constructing an argumentation graph that captures influencer justification for the takeaway; (3) performing factor graph-based probabilistic inference to validate the takeaway. We evaluate TAIGR on a content validation task over influencer video transcripts on health, showing that accurate validation requires modeling the discourse's pragmatic and argumentative structure rather than treating transcripts as flat collections of claims.
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