Information Extraction and Visualization from Twitter Considering Spatial Structure
Bibliographic record
Abstract
Mobile social media represented by Twitter are expected to be a suitable source of data for analyzing human behaviour and statuses of locations. It seems that we can provide location-based information simply by spatially filtering archived data. However, there are several problems in terms of practical use. This research considers in particular problems that concern the relationship between data meaning and their spatial structures. With regard to Twitter, in general, the location from which a tweet is posted is attached to a geotagged tweet. For example, the location coordinates attached to the geotagged tweet “Heavy rain in Miura Peninsula” by NHK (Japan's public broadcaster) are not those of the Miura Peninsula, but of Shibuya in Tokyo (where NHK is located). Therefore, the tweet is not found by a spatial search around the Miura Peninsula or even Kanagawa Prefecture (where the Miura Peninsula is located). To resolve such problems, we propose a framework that distinguishes locations of interest and locations of activity. We propose a method for automatically classifying such locations and develop a data collection, classification, and visualization system based on this method.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".