Social Media and Donations During Disasters
Bibliographic record
Abstract
Disaster response organizations (DROs) utilize social media to support fundraising efforts. They post appeals for donations and share updates on their relief efforts to engage potential donors. While prior research has shown that DROs' social media activity and public engagement can boost donations, it has typically considered these factors in isolation. However, DROs' donation appeals unfold in a dynamic environment where organizational messaging, user engagement, and public discourse may influence one another over time. Drawing on theories of prosocial behavior, we conceptualize a social media system for donations comprising DRO social media activity, public engagement, and public interest, and examine how these elements interact to influence donations over time. To test this framework, we partnered with the Canadian Red Cross and analyzed hourly Twitter activity and donation data from the 2016 Fort McMurray wildfire. Using a vector autoregressive (VAR) model, we show that DROs' tweets directly increase donations through greater impressions and clicks. Moreover, we identify three indirect pathways to donations: (1) diffusion through retweets; (2) increases in user-generated content about the disaster; and (3) heightened public interest. These indirect effects eventually surpass the direct effect such that after ten hours, they increase donations by about 35% versus 26% for the direct effect. We also find that DROs' tweets expressing positive empathy are more effective than those expressing negative empathy. Overall, our findings reveal the dynamics through which DROs' social media activity affects donations and offer guidance for designing social media strategies that help DROs raise funds for their operations.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.006 | 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".