跨平台分析15分钟城市理念的公众舆论，发现不同平台各有优劣。
A Transformer-Based Cross-Platform Analysis of Public Discourse on the 15-Minute City Paradigm
- 用压缩Transformer模型统一分析推特、Reddit和新闻的舆论
- DistilRoBERTa在情感分类上最准（F1=0.8292），TinyBERT最省时
- 新闻数据因标签失衡表现虚高，适合关注城市规划舆情的人
本研究首次对推特、Reddit和新闻媒体上关于15分钟城市概念的公众舆论进行跨平台情感分析。采用压缩Transformer模型与Llama-3-8B进行标注，实现对长文本与短文本的统一处理，支持一致标注并保证可复现评估。通过分层五折交叉验证，对比了五种模型（DistilRoBERTa、DistilBERT、MiniLM、ELECTRA、TinyBERT）的性能，报告了F1分数、AUC值及训练时间。结果表明，DistilRoBERTa在测试中达到最高F1（0.8292），TinyBERT效率最优，MiniLM跨平台一致性最佳。发现新闻数据因类别失衡导致性能被夸大，Reddit存在摘要信息丢失问题，而推特提供适中挑战。压缩模型表现不逊于大型模型，挑战了大模型必要性的假设。研究揭示各平台独特权衡，提出面向城市规划话语的可扩展、现实情感分类方向。
原文摘要 · Abstract (English)
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.
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