通过叙述检索气候变化谣言
Retrieving Climate Change Disinformation by Narrative
AI总结：
本文将叙述检测转化为检索任务，提出SpecFi框架生成假设文档以连接抽象叙述描述与具体文本，实验显示SpecFi在CARDS数据集上达到0.505 MAP，且在高变异性叙述上表现稳健。
摘要：
检测气候变化谣言叙述通常依赖固定分类，无法适应新兴叙述。因此，我们将叙述检测重新定义为检索任务：给定叙述的核心信息作为查询，按与该叙述的匹配程度对语料库中的文本进行排序。此方法无需预定义标签集，可适应新兴叙述。我们重新利用三个气候变化谣言数据集（CARDS、Climate Obstruction、PolyNarrative中的气候变化子集）进行检索评估，并提出SpecFi框架，该框架生成假设文档以弥合抽象叙述描述与具体文本实例之间的差距。SpecFi利用基于图的社区检测的社区总结作为少样本示例进行生成，在无访问叙述标签的情况下在CARDS数据集上达到0.505的MAP。我们进一步引入叙述变异性，一种基于嵌入的难度度量，并通过部分相关性分析显示，标准检索在高变异性叙述上性能下降（BM25损失63.4%的MAP），而SpecFi-CS仍保持稳健（损失32.7%）。我们的分析还表明，无监督社区总结趋向于接近专家编写的分类描述，表明基于图的方法可以从未标记文本中揭示叙述结构。
英文摘要：
Detecting climate disinformation narratives typically relies on fixed taxonomies, which do not accommodate emerging narratives. Thus, we re-frame narrative detection as a retrieval task: given a narrative's core message as a query, rank texts from a corpus by alignment with that narrative. This formulation requires no predefined label set and can accommodate emerging narratives. We repurpose three climate disinformation datasets (CARDS, Climate Obstruction, climate change subset of PolyNarrative) for retrieval evaluation and propose SpecFi, a framework that generates hypothetical documents to bridge the gap between abstract narrative descriptions and their concrete textual instantiations. SpecFi uses community summaries from graph-based community detection as few-shot examples for generation, achieving a MAP of 0.505 on CARDS without access to narrative labels. We further introduce narrative variance, an embedding-based difficulty metric, and show via partial correlation analysis that standard retrieval degrades on high-variance narratives (BM25 loses 63.4% of MAP), while SpecFi-CS remains robust (32.7% loss). Our analysis also reveals that unsupervised community summaries converge on descriptions close to expert-crafted taxonomies, suggesting that graph-based methods can surface narrative structure from unlabeled text.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
