A Multi-Level Data-driven Framework for Understanding Perceptions Towards Cycling Infrastructure Across Regions Leveraging Social Media Discourse
Shiva Azimi, Arash Tavakoli

TL;DR
This study introduces a multi-scale, data-driven framework analyzing social media discourse to understand regional perceptions of cycling infrastructure, revealing regional sentiment patterns and thematic differences across the U.S. and Europe.
Contribution
It presents a novel multi-level framework combining sentiment analysis, topic modeling, and hierarchical modeling to assess perceptions across regions using large-scale social media data.
Findings
Overall positive sentiment in both regions, slightly higher in Europe.
Sentiment more critical in comments than original posts.
Most variation in sentiment occurs within cities rather than between regions.
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…
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Taxonomy
TopicsUrban Transport and Accessibility · Urban Green Space and Health · Wildlife-Road Interactions and Conservation
