Social Media Big Data Acquisition and Analysis for Qualitative GIScience: Challenges and Opportunities
Bibliographic record
Abstract
Qualitative geographic information systems (GIS) have come a long way since the original call from critical GIS scholars in the 1990s. The invention of the geoweb as well as big data sources for qualitative information have enabled qualitative GIS to actually be implemented. Academic researchers are now grappling with how best to engage with and use qualitative spatial data. Our focus is on using qualitative data from social media sources. We review the process of collecting and analyzing patterns based on qualitative spatial data using methods from GIScience as well as new techniques from computational linguistics. We review these methods through the lens of critical qualitative GIScience. We reflect critically on the ethics associated with implementation of social qualitative data. Qualitative GIS has reached a critical juncture where the data, methods, and tools have enabled new questions to be asked that were previously not possible to pose. In this article we look to provide guidance and clarity for researchers engaging with geo-social and spatial qualitative data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.411 | 0.559 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.011 | 0.030 |
| Scholarly communication | 0.026 | 0.031 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".