摘要：社交媒体上与健康相关的讨论日益受到网红（influencers）的影响，他们针对同一议题往往提出多样化且相互竞争的观点。然而，此类内容通常是被孤立评估的，并未结合其所处的更广泛言论语境。我们提出了 GRID（Grounded Reasoning over Interconnected Discourse，基于互联言论的基础推理），这是一个通过对周边言论进行集体推断来评估网红内容的框架。GRID 将每个视频表示为一个论辩图（argumentation graph），将各项主张（claims）连接至其中心信息或核心结论（takeaway），跨视频链接相关主张和核心结论，将各项主张锚定在科学证据中，并在最终构建的网络上联合推断可信度。我们在一个基于专家评估的端到端健康相关内容验证任务上对 GRID 进行了评估，该任务涵盖 569 个网红视频。实验结果表明，结合更广泛的言论语境能有效提升网红内容的验证效果。最后，我们利用 GRID 推断的可信度，展示了低可信度与高可信度内容在论辩策略及道德框架（moral framing）上的差异。

Abstract:Health-related discussions on social media are increasingly shaped by influencers who offer diverse, and often competing perspectives on the same issue. Yet, such content is typically evaluated in isolation, without considering the broader discourse in which it appears. We introduce GRID (Grounded Reasoning over Interconnected Discourse), a framework for evaluating influencer content through collective inference over the surrounding discourse. GRID represents each video as an argumentation graph connecting claims to its central message, or takeaway, links related claims and takeaways across videos, grounds claims in scientific evidence, and jointly infers credibility over the resulting network. We evaluate GRID on an end-to-end health-related content validation task using 569 influencer videos grounded in expert assessments. Our results show that accounting for the broader discourse improves influencer content validation. Finally, we use GRID-inferred credibility to demonstrate how argumentative strategies and moral framing differ between low- and high-credibility content.