Tweet for help: the role of social media in disaster events and the case of the 2015 Mina stampede
Bibliographic record
Abstract
Social networks are important communication channel where individuals and emergency agencies can exchange information during disasters. The ability to detect disaster information or ‘reporting’ tweets would provide many advantages in disaster management during crowded events. This study explores Twitter behaviour during the Mina stampede tragedy in the 2015 Hajj by processing tweets posted over seven days during and after the incident (24–30 September 2015). Statistical features were derived from tweets, such as the number of hashtags, user mentions, and links, to provide an overview of the use of Twitter during this disaster. A classification model was built to filter reporting tweets using two Arabic natural language processing tools: Farasa and MADAMIRA. A support vector machine with a radial basis function kernel generated the best results in both tools (F-score: 88%–89%). The results will be useful to those who manage large, crowded events such as Hajj in Arabic-speaking regions.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".