Understanding your audience: The influence of social media user‐type on informational behaviors and hazard adjustments during <scp>Hurricane Dorian</scp>
Bibliographic record
Abstract
Abstract In 2019, Hurricane Dorian affected Atlantic Canada with widespread impacts across the region. In the days preceding landfall, there was a great deal of discussion about the storm and its potential impacts. This discussion also extended onto Twitter, which provided a platform for users to engage with storm‐related information. In this research, we disseminated a questionnaire to residents of Atlantic Canada from late September to late October through Qualtrics, an online survey provider. The questionnaire explored how Twitter influenced respondents' (n = 1218) self‐reported informational behaviors (i.e., searching, sharing, and processing) and behavioral responses before, during, and after the storm. The results demonstrate that users' informational needs and preferences were closely related to their online behaviors. For example, conduits (i.e., those who both searched for and shared information) were highly proactive users who disseminated information about evacuations, recommended protective actions, and other official guidance more so than others. Conduits were also the most likely to heed official guidance in terms of their own preparedness and response. Amplifiers (i.e., those who only share information) and consumers (i.e., those who only search for information) were also motivated to take action by information they saw online, albeit at lower rates than conduits. Lastly, the results demonstrate that users can be positively influenced by information they see online even if they do not actively engage with it. Taken together, the results of this study suggest that Twitter users may interact with storm‐related information in more nuanced and complex ways than previously understood.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".