A Guide to Using Social Media as a Geospatial Lens for Studying Public Opinion and Behavior

TL;DR

This paper provides a practical guide for leveraging social media data as a geospatial tool to study public opinion and behavior, highlighting workflows, methodological advances, and case studies.

Contribution

It introduces a comprehensive workflow for geospatial social media analysis and demonstrates its application through four diverse case studies.

Findings

Social media data enable timely measurement of public attitudes.

The approach supports rapid impact assessment across locations.

Fine-grained analysis of place-based experiences is achievable.

Abstract

Social media and online review platforms have become valuable sources for studying how people express opinions, report experiences, and respond to events across space. This work presents a practical guide to using user-generated social data for geospatial research on public opinion, human behavior, and place-based experience. It shows the promise of using these data as a form of passive, distributed, and human-centered sensing that complements traditional surveys and sensor systems. Methodologically, the chapter outlines a general workflow that includes platform-aware data collection, information extraction, geospatial anchoring, and statistical modeling. It also discusses how advances in large language models (LLMs) strengthen the ability to extract structured information from noisy and unstructured content. Four case studies illustrate this framework: COVID-19 vaccine acceptance, earthquake damage assessment, airport service quality, and accessibility in urban environment. Across these cases, social media data are shown to support timely measurement of public attitudes, rapid approximation of geographically distributed impacts, and fine-grained understanding of place-based experiences.