Fine-Grained Analysis of Bilingual Transportation Discourse on Reddit Using Large Language Models: A Quebec City Case Study

Social media platforms generate large volumes of unsolicited textual content that reflect users’ perceptions of urban transportation. This study proposes a fine-grained annotation framework that classifies social media text into three constructs: attitude, intention, and reported behavior. Each statement is linked to a specific transportation mode and sentiment polarity. Two large language models (LLMs), GPT-5.5 and DeepSeek-Flash, were evaluated using few-shot in-context learning on 200 manually annotated English–French Reddit discussions from Quebec City, Canada. GPT-5.5 was selected based on its mean joint relaxed F1-score and applied to 2475 remaining discussions. The model extracted 23,634 transportation-mode mentions and 10,760 mode-specific construct statements after deduplication. Public transportation was the most frequently mentioned category and had the highest within-mode proportion of negative statements, although its positive statements outnumbered its negative statements. Attitude was the most frequently identified construct, while intention was the least frequent. The framework distinguishes evaluations, future plans, and self-reported actions or experiences, providing a more detailed description of transportation discourse. Given the Reddit sample and remaining extraction errors, these findings are exploratory and may complement other evidence used in sustainable urban mobility planning.