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 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 | 0.009 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".