Detecting Online Firestorms in Social Media
Bibliographic record
Abstract
As social media has increased the reach and speed of electronic word-of-mouth (eWOM), so it has intensified customers’ exposure to negative eWOM. Consequently, companies increasingly suffer from massive outbursts of negative eWOM, known as online firestorms. Because of their dynamics, it is nearly impossible to stop online firestorms if their emergence is not detected promptly. However, well-founded approaches that provide automated, real-time detection are missing. We design an Online Firestorm Detector that includes an algorithm inspired by epidemiological surveillance systems. Real-world data from a firestorm suffered by Coca-Cola is used to prove the utility and validity of the proposed approach. We show that online firestorms can be reliably detected shortly after the first piece of related negative eWOM has been generated, and that the number of false alarms is low. A comparison with competing artifacts shows that the Online Firestorm Detector is superior to approaches that could be alternatively used.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".