Invisible Walls in Cities: Leveraging Large Language Models to Predict Urban Segregation Experience with Social Media Content

• URL: http://arxiv.org/abs/2503.04773v2
• Date: Mon, 10 Mar 2025 06:15:16 GMT
• Title: Invisible Walls in Cities: Leveraging Large Language Models to Predict Urban Segregation Experience with Social Media Content
• Authors: Bingbing Fan, Lin Chen, Songwei Li, Jian Yuan, Fengli Xu, Pan Hui, Yong Li,
• Abstract summary: We propose using Large Language Models to automate online review mining for segregation prediction.<n>We produce a codebook capturing key dimensions that signal segregation experience, such as cultural resonance and appeal.<n>Our framework greatly improves prediction accuracy, with a 22.79% elevation in R2 and a 9.33% reduction in MSE.
• Score: 21.34394159491078
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 using Large Language Models (LLMs) to automate online review mining for segregation prediction. We design 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 framework greatly improves prediction accuracy, with a 22.79% elevation in R2 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 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 AI.

Related papers

• SCRAG: Social Computing-Based Retrieval Augmented Generation for Community Response Forecasting in Social Media Environments [8.743208265682014]
  SCRAG is a prediction framework inspired by social computing. It forecast community responses to real or hypothetical social media posts. It can be used by public relations specialists to craft messaging in ways that avoid unintended misinterpretations.
  arXiv  Detail & Related papers  (2025-04-18T15:02:31Z)
• InclusiViz: Visual Analytics of Human Mobility Data for Understanding and Mitigating Urban Segregation [41.758626973743525]
  InclusiViz is a novel visual analytics system for multi-level analysis of urban segregation.<n>We developed a deep learning model to predict mobility patterns across social groups using environmental features, augmented with explainable AI.<n>The system integrates innovative visualizations that allow users to explore segregation patterns from broad overviews to fine-grained detail.
  arXiv  Detail & Related papers  (2025-01-07T07:50:36Z)
• Evaluating Cultural and Social Awareness of LLM Web Agents [113.49968423990616]
  We introduce CASA, a benchmark designed to assess large language models' sensitivity to cultural and social norms.<n>Our approach evaluates LLM agents' ability to detect and appropriately respond to norm-violating user queries and observations.<n>Experiments show that current LLMs perform significantly better in non-agent environments.
  arXiv  Detail & Related papers  (2024-10-30T17:35:44Z)
• Social Debiasing for Fair Multi-modal LLMs [55.8071045346024]
  Multi-modal Large Language Models (MLLMs) have advanced significantly, offering powerful vision-language understanding capabilities. However, these models often inherit severe social biases from their training datasets, leading to unfair predictions based on attributes like race and gender. This paper addresses the issue of social biases in MLLMs by i) Introducing a comprehensive Counterfactual dataset with Multiple Social Concepts (CMSC) and ii) Proposing an Anti-Stereotype Debiasing strategy (ASD)
  arXiv  Detail & Related papers  (2024-08-13T02:08:32Z)
• MuseCL: Predicting Urban Socioeconomic Indicators via Multi-Semantic Contrastive Learning [13.681538916025021]
  MuseCL is a framework for fine-grained urban region profiling and socioeconomic prediction. We construct contrastive sample pairs for street view and remote sensing images, capitalizing on similarities in human mobility. We extract semantic insights from POI texts embedded within these regions, employing a pre-trained text encoder.
  arXiv  Detail & Related papers  (2024-06-23T09:49:41Z)
• Ask LLMs Directly, "What shapes your bias?": Measuring Social Bias in Large Language Models [11.132360309354782]
  Social bias is shaped by the accumulation of social perceptions towards targets across various demographic identities. We propose a novel strategy to intuitively quantify social perceptions and suggest metrics that can evaluate the social biases within large language models.
  arXiv  Detail & Related papers  (2024-06-06T13:32:09Z)
• Chain-of-Thought Prompting for Demographic Inference with Large Multimodal Models [58.58594658683919]
  Large multimodal models (LMMs) have shown transformative potential across various research tasks. Our findings indicate LMMs possess advantages in zero-shot learning, interpretability, and handling uncurated 'in-the-wild' inputs. We propose a Chain-of-Thought augmented prompting approach, which effectively mitigates the off-target prediction issue.
  arXiv  Detail & Related papers  (2024-05-24T16:26:56Z)
• No Filter: Cultural and Socioeconomic Diversity in Contrastive Vision-Language Models [38.932610459192105]
  We study cultural and socioeconomic diversity in contrastive vision-language models (VLMs) Our work underscores the value of using diverse data to create more inclusive multimodal systems.
  arXiv  Detail & Related papers  (2024-05-22T16:04:22Z)
• Bias and Fairness in Large Language Models: A Survey [73.87651986156006]
  We present a comprehensive survey of bias evaluation and mitigation techniques for large language models (LLMs) We first consolidate, formalize, and expand notions of social bias and fairness in natural language processing. We then unify the literature by proposing three intuitive, two for bias evaluation, and one for mitigation.
  arXiv  Detail & Related papers  (2023-09-02T00:32:55Z)
• Training Socially Aligned Language Models on Simulated Social Interactions [99.39979111807388]
  Social alignment in AI systems aims to ensure that these models behave according to established societal values. Current language models (LMs) are trained to rigidly replicate their training corpus in isolation. This work presents a novel training paradigm that permits LMs to learn from simulated social interactions.
  arXiv  Detail & Related papers  (2023-05-26T14:17:36Z)
• Towards Understanding and Mitigating Social Biases in Language Models [107.82654101403264]
  Large-scale pretrained language models (LMs) can be potentially dangerous in manifesting undesirable representational biases. We propose steps towards mitigating social biases during text generation. Our empirical results and human evaluation demonstrate effectiveness in mitigating bias while retaining crucial contextual information.
  arXiv  Detail & Related papers  (2021-06-24T17:52:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.