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

2026-03-17

AI Summary

This study addresses the challenge of comparing public perceptions of cycling infrastructure across large spatial scales. To overcome the limitations of traditional single-city surveys, the authors propose a multiscale data-driven framework that integrates sentiment analysis, topic modeling, and aspect-based sentiment classification with hierarchical statistical modeling. Applying this approach to over 30,000 Reddit posts and more than 500,000 comments from multiple cities in the United States and Europe, the research pioneers the combination of cross-regional social media discourse with multilevel modeling. Findings reveal an overall positive sentiment toward cycling in both regions—slightly higher in Europe—with comments exhibiting greater criticism than original posts. Notably, perceptual differences are primarily driven by intra-urban variation rather than inter-regional disparities.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Knowledge Representation and Reasoning: Preferences

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

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.

Problem

• cycling infrastructure
• public perception
• social media discourse
• geographic comparison
• urban mobility

Innovation

• multi-scale framework
• social media discourse
• sentiment analysis
• aspect-based classification
• cross-regional comparison