Branding in the age of social media firestorms: how to create brand value by fighting back online
Bibliographic record
Abstract
Leading research on social media firestorms typically advises managers to quickly quell the backlash by appeasing brand critics. Drawing on crisis communications and branding research, we offer a radically different perspective and argue that brands can benefit from fighting back online. Through a netnography of a moral-based firestorm, we contribute to the marketing and crisis communications literatures by identifying the escalation strategy as a way to build brand value; explaining how brands can activate supporters; and providing guidance on how to assess these morally steeped events. We advance branding research by identifying how managers can provoke consumer-generated brand stories; and uncovering the hidden benefits of negative consumer voices. Finally, we outline a new perspective on how brands are dialogically constructed through a process we call ‘flyting’.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.016 | 0.027 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".