用社交媒体分析城市隐性隔离，让大模型自动识别不平等空间体验
Invisible Walls in Cities: Designing LLM Agent to Predict Urban Segregation Experience with Social Media Content
- 设计反思型LLM编码器，将社交内容提炼为可解释的隔离维度
- 在三个城市验证，预测准确率提升22.79%（R²），误差降低9.33%（MSE）
- 生成的代码本帮助用户更好感知场所的社会包容性，适合城市规划者
理解城市日常生活中体验到的隔离现象对解决社会不平等、促进包容性至关重要。社交媒体上的用户生成评论蕴含了不同地点的细微感知与情感，为揭示隔离提供了丰富线索。然而，由于数据量大、语义模糊且观点多样，利用这些数据面临巨大挑战。为此，我们提出一种新型大语言模型（LLM）代理，用于自动化挖掘在线评论以预测隔离。具体地，我们设计了一个反思型LLM编码器，将社交媒体内容转化为与真实反馈一致的洞察，并生成一个代码本，捕捉如文化共鸣与吸引力、可达性与便利性、社区参与与本地投入等关键隔离维度。基于该代码本，LLM可生成有信息量的评论摘要和评分用于隔离预测。此外，我们提出一种推理与嵌入融合框架（RE'EM），结合语言模型的推理与嵌入能力，整合多通道特征进行预测。在真实数据上的实验表明，该代理显著提升了预测准确率，R²提升22.79%，均方误差降低9.33%。所获代码本在三个不同城市具有泛化能力，持续提升预测性能。用户研究进一步证实，代码本引导的摘要能显著提升人类参与者对兴趣点（POIs）社会包容性的认知。本研究标志着理解隐性社会壁垒的重要进展，展示了网络技术推动社会包容的巨大潜力。
原文摘要 · Abstract (English)
Understanding experienced segregation in urban daily life is crucial for addressing societal inequalities and fostering inclusivity. The abundance of user-generated reviews on social media encapsulates nuanced perceptions and feelings associated with different places, offering rich insights into segregation. However, leveraging this data poses significant challenges due to its vast volume, ambiguity, and confluence of diverse perspectives. To tackle these challenges, we propose a novel Large Language Model (LLM) agent to automate online review mining for segregation prediction. Specifically, we propose a reflective LLM coder to digest social media content into insights consistent with real-world feedback, and eventually produce a codebook capturing key dimensions that signal segregation experience, such as cultural resonance and appeal, accessibility and convenience, and community engagement and local involvement. Guided by the codebook, LLMs can generate both informative review summaries and ratings for segregation prediction. Moreover, we design a REasoning-and-EMbedding (RE'EM) framework, which combines the reasoning and embedding capabilities of language models to integrate multi-channel features for segregation prediction. Experiments on real-world data demonstrate that our agent substantially improves prediction accuracy, with a 22.79% elevation in R$^{2}$ and a 9.33% reduction in MSE. The derived codebook is generalizable across three different cities, consistently improving prediction accuracy. Moreover, our user study confirms that the codebook-guided summaries provide cognitive gains for human participants in perceiving places of interest (POIs)' social inclusiveness. Our study marks an important step toward understanding implicit social barriers and inequalities, demonstrating the great potential of promoting social inclusiveness with Web technology.
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