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

Researchers Shiva Azimi and Arash Tavakoli have developed a novel AI-powered framework that leverages large-scale social media data to understand public perceptions of cycling infrastructure across different regions. Their system analyzes over 30,000 Reddit posts and 500,000 associated comments from cycling-focused 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 framework examines how cycling infrastructure is discussed and evaluated in online public discourse at multiple spatial scales.

The study reveals several key insights about cycling sentiment patterns. Overall sentiment toward cycling is positive in both regions, with slightly higher values observed in the European sample, though differences remain modest. Interestingly, sentiment tends to become more critical in comment discussions compared to original posts. The analysis shows that sentiment is primarily associated with experience-based themes, with most variation occurring within cities rather than between regions. This suggests that local context and individual experiences play a more significant role in shaping perceptions than broad regional differences.

The framework represents a significant advancement over traditional survey-based approaches, enabling researchers to analyze public opinion at unprecedented scale and granularity. By examining how discussions unfold across broader geographic contexts, the system provides insights that were previously difficult to obtain through conventional methods focused on individual cities. The multi-scale approach allows for simultaneous analysis of local, regional, and cross-national patterns in cycling discourse.

This research demonstrates how discussion-based online data can complement traditional approaches to understanding public perceptions of urban infrastructure. The findings have important implications for urban planners, policymakers, and transportation researchers seeking to design cycling infrastructure that aligns with community needs and preferences. The framework's methodology could potentially be adapted to study perceptions of other types of urban infrastructure and public services.