Geolocation Prediction in Twitter Using Social Networks: A Critical Analysis and Review of Current Practice
Bibliographic record
Abstract
Geolocated social media data provides a powerful source of information about place and regional human behavior. Because little social media data is geolocation-annotated, inference techniques serve an essential role for increasing the volume of annotated data. One major class of inference approaches has relied on the social network of Twitter, where the locations of a user's friends serve as evidence for that user's location. While many such inference techniques have been recently proposed, we actually know little about their relative performance, with the amount of ground truth data varying between 5% and 100% of the network, the size of the social network varying by four orders of magnitude, and little standardization in evaluation metrics. We conduct a systematic comparative analysis of nine state-of-the-art network-based methods for performing geolocation inference at the global scale, controlling for the source of ground truth data, dataset size, and temporal recency in test data. Furthermore, we identify a comprehensive set of evaluation metrics that clarify performance differences. Our analysis identifies a large performance disparity between that reported in the literature and that seen in real-world conditions. To aid reproducibility and future comparison, all implementations have been released in an open source geoinference package.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".