Speak with One Voice? Examining Content Coordination and Social Media Engagement During Disasters
Bibliographic record
Abstract
Speak with One Voice? Examining Content Coordination and Social Media Engagement During Disasters Practice- and policy-oriented abstract: Disaster relief organizations (DROs) use social media to share information rapidly and broadly. Many DROs maintain multiple accounts on the same social media platform. Each account represents a different operational entity of a DRO, such as its national headquarters or a local branch. An important problem that DROs with multiple accounts face is how to coordinate the production of social media content across these accounts. One strategy is to have all accounts within the same DRO match their decisions about how content is created and designed (e.g., audience, topic). An alternate strategy is to mismatch these decisions. Using Twitter data collected in partnership with the Canadian Red Cross, we analyze which coordination strategy is best for social media engagement. Our results suggest that, during the immediate aftermath of a disaster, accounts within the same DRO should produce content with similar characteristics by matching their content creation decisions. This leads to a 4.3% lift in engagement. However, when DROs start working on helping impacted communities recover from disasters, engagement is 29.6% higher when their accounts mismatch their content creation decisions and post distinctive content.
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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 | 0.007 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| 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".