Rise of Digital Humanitarian Network (DHN) in Southeast Asia: Social Media Insights for Crisis Mapping in Disaster Risk Reduction (DRR)
Bibliographic record
Abstract
Digital technologies and big data speedily change humanitarian crisis response and transform the processes from traditional to digital. Digital Humanitarian Network (DHN) for disaster risk reduction (DRR) using crisis mapping of the vulnerable population is becoming increasingly common during any disaster response process. To get the information and provide in time support, the critical Source of data is social media. In Southeast Asia, Facebook is the most used social media platform. Communities often rely on social media to seek in time assistance and guidance. Emerging social media and networks are remarkably well-compatible with intelligent data-centric systems, which foster an effective disaster management plan under disaster scenarios. During previous disasters in Southeast Asia, it was believed to be the fastest response medium. However, validation is essential to obtain important data, and after years of research, there are still many undiscovered features of social media that can be used in emergencies. This paper aims to determine Southeast Asian countries' readiness to utilise social media for DRR activities and understand the criteria of DHN by integrating crisis mapping. A qualitative research design is applied to gain an insight into the humanitarian disaster network for disaster risk reduction. Data were collected through document analysis. I argue that digital humanitarians can offer a unique combination of speed and safe access while escaping some of the traditional constraints of the aid-media relationship. The study concluded that DHN provides a collaborative environment for the organizations to collaborate and act fast to assist.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".