Problem
Research questions and friction points this paper is trying to address.
social media
geospatial analysis
public opinion
human behavior
place-based experience
A Guide to Using Social Media as a Geospatial Lens for Studying Public Opinion and Behavior
🤖 AI Summary
This study addresses the limitations of traditional surveys and sensor-based approaches in capturing public opinion and behavior by proposing a geospatial analysis framework leveraging social media data. Treating social media as a passive, distributed, and human-centered sensing system, the framework employs large language models to extract structured information from unstructured text, followed by geolocation anchoring and statistical modeling to enable dynamic analysis. Validated through four case studies—vaccine acceptance, earthquake damage assessment, airport service quality, and urban accessibility—the approach effectively supports real-time monitoring of public sentiment, rapid evaluation of geospatial impacts, and fine-grained characterization of place-based experiences, substantially enhancing both the timeliness and spatial precision of social sensing.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
social media sensing
geospatial anchoring
large language models
passive sensing
human-centered data
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