Fighting the social media wildfire: how crisis communication must adapt to prevent from fanning the flames
Bibliographic record
Abstract
When a nine-month correspondence seeking reparations for musical instruments damaged by United Airlines employees stalemated, Canadian musician Dave Carroll took action online. Utilizing the video-sharing Web site YouTube, Carroll narrated his ordeal through the lyrics of a music video entitled United Breaks Guitars. Within hours, the video went viral generating a torrent of negative YouTube comments about United, commentary from the mainstream media, and more than 3 million views the first week of its launch. United Breaks Guitars embodies the new phenomenon of a social media wildfire in which the rapid proliferation of information through social media causes severe reputational damage to organizations whose crisis communication plans are ill equipped to handle online dilemmas. Using symbolic interactionist theory, this case analysis explores the phenomenon in detail and provides suggestions for how organizations must re-evaluate existing crisis communication plans to respond effectively to an online audience in the billions.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.042 |
| Scholarly communication | 0.025 | 0.022 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.009 | 0.010 |
| 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".