Beyond Moral Outrage: The Role of the Ingroup in Online Condemnations
Bibliographic record
Abstract
ABSTRACT Although expressions of condemnation are pervasive on social media and often shape the narratives of traditional news outlets, brands continue to struggle with predicting and managing these responses effectively. To address this challenge, it is crucial to first understand the underlying reasons for such behavior. While existing research argues that moral outrage alone is sufficient to drive online condemnations, we present a more nuanced approach as to when and why this behavior occurs. Across six studies (including Supplementary Appendix A; N = 1285) we argue and find that the perception that there is a like‐minded ingroup to whom one can signal is critical for promulgating online condemnations. Notably, this audience must be perceived to feel similarly outraged as the condemner (studies 1a‐c), and the condemner must be able to signal to this audience through a public post (vs. merely expressing their condemnation privately with no audience; study 2). Engaging in such condemnations predicts subsequent actual consumption choices (study 3), with substantive implications for marketers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".