Social media during multi-hazard disasters: Lessons from the Kaikoura earthquake 2016
Bibliographic record
Abstract
Social media provides channels of communication during emergency events such as earthquakes.Such sites may be utilised for a range of emergency response strategies providing that data is processed rapidly and management strategies employed effectively.The processing of social media data presents many challenges for emergency responders: information overload, organisational communication and information reliability remain prevalent issues.Furthermore, there is a growing need to improve the management of multi-hazard disasters (sometimes referred to as 'cascading disasters') due to an increase in their frequency and severity, exacerbated by underlying global problems such as climate change.This is especially important to geographical regions that are prone to particular hazards -New Zealand for instance recorded nearly 33,000 earthquakes in 2016 alone.Similarly, there is an increasing need to evaluate developments in technology and social media sites themselves as they are progressively being relied upon during emergency events.In this study, we examine the crisis communications of the Kaikoura earthquake (New Zealand, 2016) using mainstream media content such as new stories, and online content such as Twitter data.A mixed method approach was employed, which combined content analysis with the application of a conceptual framework.The paper then presents (i) an analysis of crisis communications during the event, focusing on changes in media content and theme, (ii) the structure of online emergency response in the country and its affect on management and (iii) the barriers effecting emergency response in this case study.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".