An Empirical Text Mining Analysis of Fort McMurray Wildfire Disaster Twitter Communication using Topic Model
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
Twitter has emerged as one of the most preferred disaster communication medium particularly in those countries where it has significant presence. Fort McMurray, Canada was engulfed in a wildfire in May 2016 that burnt down major part of the city and the disaster communication on twitter related to this was studied. Tweets related were downloaded and analyzed using frequency analysis, correlation analysis and Topic Model Latent Dirichlet Allocation (Gibbs Method). The LDA model automatically discovered the most relevant topics that are highly correlated probabilistically to the effective and reliable disaster communication. The disaster communication pattern also followed the various disaster stages as initially most of the information was related to intensity, evacuation and relief efforts followed by the updates and status of the wildfire and firefighting efforts and finally related to phased returning of residents back to city.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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