Effects of Disaster Characteristics on Twitter Event Signature
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 a platform that is heavily used during disasters. Therefore, as an event unfolds, it generates varying levels of online engagement from victims as well as onlookers (both physical and virtual). Because methods for mining disaster-related content at scale must contend with the problem of filtering out vast numbers of unrelated posts, any prior knowledge about the characteristics of disaster-related content in the live Twitter feed may help improve the recovery of relevant posts. In this study, we consider the relative abundance of a disasters Twitter content over time (both relative to total event-related content and relative to the overall volume of content generated on Twitter). We refer to this time-varying abundance as the events signature. In an analysis of three different disasters, we find that event signatures are qualitatively different. These differences can be explained in terms of several characteristics of disasters: foreknowledge, duration, severity, and news media engagement.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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