A Transformer-Based Cross-Platform Analysis of Public Discourse on the 15-Minute City Paradigm
This work addresses scalable sentiment classification for urban planning discourse, but it is incremental as it applies existing methods to a new multi-platform dataset.
This study tackled the problem of analyzing public sentiment on the 15-minute city concept across multiple platforms by using compressed transformer models, achieving an F1-score of 0.8292 with DistilRoBERTa and identifying platform-specific performance trade-offs.
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.