Using social media to geo-target emergency management efforts
Bibliographic record
Abstract
The ubiquity of social media data has increased their use during emergency management (EM) in near real-time. For instance, during Haiti Earthquake (2010) and Hurricane Harvey (2017), Ushahidi and Twitter were used respectively for emergency response and rescue operations. Nonetheless, social media data tend to contain [ir]relevant information to be useful for disaster analytics for EM efforts. In this study, geo-tagged tweets obtained for 2013 Colorado flooding were analyzed to determine (i) what kind of situational awareness (SA) information could be extracted from tweets for emergency response and (ii) what is the spatio-temporal distribution of such information. The results indicate that tweets generated before September 12th (day of heavy precipitation) were non-relevant, but tweets generated on and following September 12th contained crisis information (about the event and its impacts), warnings, preparatory information. Next phase of this study will focus on developing a framework to integrate SA information with physical risk and social vulnerability to geo-target EM efforts.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".