Trustworthiness Perceptions of Social Media Resources Named after a Crisis Event
Bibliographic record
Abstract
People often create social media accounts and pages named after crisis events. We call such accounts and pages Crisis Named Resources (CNRs). CNRs share information about crisis events and are followed by many. Yet, they also appear suddenly (at crisis onset) and in most cases, the owners are unknown. Thus, it can be challenging for audiences in particular to know whether to trust (or not trust) these CNRs and the information they provide. In this study, we conducted surveys and interviews with members of the public and experts in crisis informatics, emergency response, and communication studies to evaluate the trustworthiness of CNRs named after the 2017 Hurricane Irma. Findings showed that participants evaluated trustworthiness based on their perceptions of a CNR's content, information source, profile, and owner. Findings also show that if people perceive that a CNR owner has prior experience in crisis response, can help the public to respond to the event, understands the situation, has the best interests of affected individuals in mind, or will correct misinformation, they tend to trust that CNR. Participant demographics and expertise showed no effect on perceptions of trustworthiness.
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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 | 0.000 | 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 | 0.001 | 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".