A Multi-Level Data-driven Framework for Understanding Perceptions Towards Cycling Infrastructure Across Regions Leveraging Social Media Discourse

Abstract

Cycling plays an important role in sustainable urban mobility, yet how people perceive cycling infrastructure varies widely and remains challenging to assess at large spatial scales. Existing research has mainly relied on surveys or short-form social media data and has often focused on individual cities, leaving limited insight into how cycling discussions unfold across broader geographic contexts. This study proposes a multi-scale framework that examines how cycling infrastructure is discussed and evaluated in online public discourse and explores whether sentiment patterns differ between the United States (U.S.) and selected European countries included in the dataset. The analysis draws on a large collection of discussions on a social media platform, namely Reddit, including more than 30,000 posts and over 500,000 associated comments gathered from cycling-focused and geographically defined communities across multiple U.S. states and selected European countries. Using a combination of sentiment analysis, topic modeling, aspect-based classification, and hierarchical statistical modeling, the study evaluates the emotional tone and thematic structure of these discussions and how they vary spatially. Overall sentiment toward cycling is positive in both regions, with slightly higher values observed in the European sample, although differences remain modest. Sentiment tends to become more critical in comment discussions compared to original posts. Topic and aspect analyses show that sentiment is primarily associated with experience-based themes, with most variation occurring within cities rather than between regions. Together, these findings illustrate how discussion-based online data can complement traditional approaches to understanding public perceptions of cycling infrastructure in sustainable urban contexts.

Figures (10)

• Figure 1: Overview of the data collection, preprocessing, and analysis workflow used in this study.
• Figure 2: Spatial distribution of Reddit posts in the dataset. (A) Post density across U.S. states and (B) post density across European countries. Labels indicate geographic unit names and total post counts. Map geometries are adjusted where necessary to improve visualization clarity and readability.
• Figure 3: Word clouds of cycling-related Reddit posts in the United States and Europe. Word size reflects relative term frequency after text preprocessing and stopword removal.
• Figure 4: Number of cycling-related posts by infrastructure aspect in Europe and the United States.
• Figure 5: Mean sentiment scores by cycling infrastructure aspect in Europe and the United States.