用动态方法从社交媒体中挖掘观点证据，理解不同群体的抽象思维差异。
ConceptCarve: Dynamic Realization of Evidence
- 结合传统检索器与大模型，动态刻画搜索空间
- 在社交媒体中更精准找到观点证据，效果优于传统方法
- 生成可解释的证据表示，适合研究社会认知差异
大规模发现人类观点与行为的证据是一项挑战，常需理解社交媒体上庞大社群中的复杂思维模式。例如，研究枪支拥有与自由观念的关系，需要一个能在海量社交帖子中运行的检索系统，并应对两大难题：(1) 识别抽象概念的具体实例，(2) 这些实例在不同社群中可能呈现不同形态。为此，我们提出 ConceptCarve，一种利用传统检索器和大语言模型在检索过程中动态刻画搜索空间的证据检索框架。实验表明，ConceptCarve 在社交媒体社群中发现证据的能力超越传统检索系统，同时生成该社群可解释的证据表示，用于定性分析不同社群间复杂的思维模式差异。
原文摘要 · Abstract (English)
Finding evidence for human opinion and behavior at scale is a challenging task, often requiring an understanding of sophisticated thought patterns among vast online communities found on social media. For example, studying how gun ownership is related to the perception of Freedom, requires a retrieval system that can operate at scale over social media posts, while dealing with two key challenges: (1) identifying abstract concept instances, (2) which can be instantiated differently across different communities. To address these, we introduce ConceptCarve, an evidence retrieval framework that utilizes traditional retrievers and LLMs to dynamically characterize the search space during retrieval. Our experiments show that ConceptCarve surpasses traditional retrieval systems in finding evidence within a social media community. It also produces an interpretable representation of the evidence for that community, which we use to qualitatively analyze complex thought patterns that manifest differently across the communities.
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