Of Bubbles and Sentiments: Virtual Communities in the Aftermath of Dorian
Bibliographic record
Abstract
The study investigated the structural qualities of the dominant virtual Twitter communities enduring in the aftermath of a natural disaster and how they influence the flow of information among social actors in the network. By employing a combination of textual and social network analyses on tweets associated with Hurricane Dorian, the study reinforces the findings of previous studies that information propagation is determined by nature of interactional communities built in the different stages of an emergency event and that sentiments and choice of user message keywords follow along the lines of geographical proximity to the affected zones. Engagements among social actors led to formation of virtual communities that were found to be dominated by hierarchical, polarized and insulated structural features which characteristically determine their information propagation patterns. These information community clusters demonstrate highly defined boundaries with sparse overlaps. Also, political and media actors demonstrate the most influences during this phase of the disaster. Implications of these findings for both research and practice as well as the limitations of research findings were discussed. L'étude a examiné les caracteristiques structurelles des communautés virtuelles dominantes sur Twitter suite à une catastrophe naturelle et comment elles influencent le flux d'informations entre les acteurs du réseau. En employant une combinaison d'analyses textuelles et de réseaux sociaux sur les tweets associés à l'ouragan Dorian, l'étude renforce les conclusions des recherches antérieures selon lesquelles la propagation de l'information est déterminée par la nature des communautés interactionnelles construites aux différentes étapes d'une urgence et que les émotions et le choix de mots-clés des utilisateurs sont liés au degré de proximité géographique des zones touchées. L'engagement des acteurs conduit à la formation de communautés virtuelles qui se s'avèrent dominées par des caractéristiques structurelles hiérarchiques, polarisées et isolées qui déterminent les modes de propagation de l'information. Ces communautés présentent des limites très définies avec des chevauchements éparses. De plus, les acteurs politiques et médiatiques sont ceux qui ont le plus d'influence durant la catastrophe. Les implications de ces résultats pour la recherche et la pratique ainsi que les limites des résultats de la recherche sont discutés.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".