# Leveraging Multi-Source Textural UGC for Neighbourhood Housing Quality Assessment: A GPT-Enhanced Framework

arXiv: https://arxiv.org/abs/2508.16657 · HTML: https://mlsanity.com/paper/2508.16657/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 10 references that are in the map.
- First posted on arXiv 20 Aug 2025.

## Builds on (most cited first, 1 of 1)

- [Cross-Platform Social Dynamics: An Analysis of ChatGPT and COVID-19 Vaccine Conversations](https://mlsanity.com/paper/2310.11116/) (2023)

## Abstract (arXiv)

This study leverages GPT-4o to assess neighbourhood housing quality using multi-source textural user-generated content (UGC) from Dianping, Weibo, and the Government Message Board. The analysis involves filtering relevant texts, extracting structured evaluation units, and conducting sentiment scoring. A refined housing quality assessment system with 46 indicators across 11 categories was developed, highlighting an objective-subjective method gap and platform-specific differences in focus. GPT-4o outperformed rule-based and BERT models, achieving 92.5% accuracy in fine-tuned settings. The findings underscore the value of integrating UGC and GPT-driven analysis for scalable, resident-centric urban assessments, offering practical insights for policymakers and urban planners.