The Role of Social Media in the Process of Informing the Public About Disaster Risks
Bibliographic record
Abstract
Social media informs the public about the most important events and conveys important information. Before, during and after disasters, social media are used to disseminate information about disasters and to collect data relevant to the implementation of preparedness, response and recovery activities and measures. Social networks are effective in disseminating information and warnings, as well as in educating the public. The subject of the research is examining the influence of demographic factors on the effectiveness of social media in informing the public about the risks of disasters. Using an online survey questionnaire, and according to the snowball principle, a survey of 247 respondents was conducted in 2022. The research results show no statistically significant relationship between the level of education of the respondents and the assessment of the effectiveness of social media reporting on disasters. The use of social media can improve communication between stakeholders in disaster management and facilitate coordination of efforts, fostering communication and allocation of resources. To effectively use social media in disaster management, decision-makers in the disaster management system must be aware of new technologies, their disadvantages and advantages, as well as ways in which data from social networks can be collected and analyzed.
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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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".