A Guide to Using Social Media as a Geospatial Lens for Studying Public Opinion and Behavior
It provides a methodological framework for researchers in social sciences and geospatial analysis to leverage social media data as a complement to traditional methods, though it is incremental as it builds on existing data sources and techniques.
This work tackles the problem of studying public opinion and behavior across space by presenting a practical guide for using social media data as a geospatial lens, showing that it supports timely measurement of public attitudes, rapid impact assessment, and fine-grained understanding of place-based experiences through case studies like COVID-19 vaccine acceptance and earthquake damage.
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.