Area-Based Topic Modeling and Visualization of Social Media for Qualitative GIS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it