Calming the Storm: How Non-Negative Messages From Fellow Consumers Can Dispel Negativity in a Social Media Firestorm
Bibliographic record
Abstract
This research explores the potential of non-negative consumer messages to counteract negativity in social media firestorms through emotional contagion. 1,186 tweets were examined in response to a McDonald’s Japan service issue, revealing that non-negative messages tend to align emotionally with preceding messages. This suggests a temporary mitigation of negativity. Investigating emotional contagion within social media firestorms challenged the prevailing notion of negativity bias, indicating a focus on maintaining a positive affective state. Practical implications suggest organizations should monitor and acknowledge non-negative messages during crises to identify advocates and gain insights into subnetwork impact. Incorporating elements from contagious non-negative posts in responses can help mitigate reputational damage. This research contributes to a deeper understanding of emotional contagion dynamics in social media firestorms, aiding organizations in managing their online reputation during crises.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".