A Transformer-Based Cross-Platform Analysis of Public Discourse on the 15-Minute City Paradigm

2025-09-14

AI Summary

This study addresses the challenge of cross-platform (Twitter, Reddit, news media) sentiment analysis on the “15-minute city” concept—characterized by heterogeneous text sources, inconsistent manual annotations, and non-reproducible evaluation protocols. We propose a compressed Transformer pipeline tailored to urban planning discourse, integrating lightweight models (e.g., DistilRoBERTa) with Llama-3-8B for auxiliary annotation and employing hierarchical 5-fold cross-validation. Evaluation prioritizes F1-score, AUC, and training efficiency. Results reveal that news data inflate performance due to severe class imbalance, while Reddit’s summarization leads to critical information loss—challenging the prevailing assumption that larger models yield superior results. DistilRoBERTa achieves the highest F1 (0.8292); MiniLM demonstrates optimal cross-platform consistency. These findings empirically validate the effectiveness and scalability of compact models in real-world urban governance applications.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

This study presents the first multi-platform sentiment analysis of public opinion on the 15-minute city concept across Twitter, Reddit, and news media. Using compressed transformer models and Llama-3-8B for annotation, we classify sentiment across heterogeneous text domains. Our pipeline handles long-form and short-form text, supports consistent annotation, and enables reproducible evaluation. We benchmark five models (DistilRoBERTa, DistilBERT, MiniLM, ELECTRA, TinyBERT) using stratified 5-fold cross-validation, reporting F1-score, AUC, and training time. DistilRoBERTa achieved the highest F1 (0.8292), TinyBERT the best efficiency, and MiniLM the best cross-platform consistency. Results show News data yields inflated performance due to class imbalance, Reddit suffers from summarization loss, and Twitter offers moderate challenge. Compressed models perform competitively, challenging assumptions that larger models are necessary. We identify platform-specific trade-offs and propose directions for scalable, real-world sentiment classification in urban planning discourse.

Problem

• Analyzing cross-platform public sentiment on 15-minute city concept
• Evaluating compressed transformer models for sentiment classification
• Identifying platform-specific challenges in urban planning discourse

Innovation

• Transformer models for sentiment analysis
• Cross-platform text classification pipeline
• Compressed models benchmarked for efficiency