Area-Based Topic Modeling and Visualization of Social Media for Qualitative GIS
Bibliographic record
Abstract
Qualitative geographic information systems (GIS) has progressed in meaningful ways since early calls for a qualitative GIS in the 1990s. From participatory methods to the invention of the participatory geoweb and finally to geospatial social media sources, the amount of information available to nonquantitative GIScientists has grown tremendously. Recently, researchers have advanced qualitative GIS by taking advantage of new data sources, like Twitter, to illustrate the occurrence of various phenomena in the data set geospatially. At the same time, computer scientists in the field of natural language processing have built increasingly sophisticated methods for digesting and analyzing large text-based data sources. In this article, the authors implement one of these methods, topic modeling, and create a visualization method to illustrate the results in a visually comparative way, directly onto the map canvas. The method is a step toward making the advances in natural language processing available to all GIScientists. The article discusses the ways in which geography plays an important part in understanding the results presented from the model and visualization, including issues of place and space.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".