Overuse of Moral Language Dampens Content Engagement on Social Media
Bibliographic record
Abstract
Online social-media platforms are a vital arena in which socio-political perspectives are put forth, debated, and spread. Prior work suggests that certain moral-emotional language drives online contagion, but theoretical and empirical findings remain debated, inhibiting constructive interventions. We substantially advance this ongoing debate using a diverse range of topics and both mainstream and extremist social media platforms. We find two countervailing dynamics predict the rise and fall of posting engagement online. First, we confirm that content infused with a greater number of moral words is associated with increased engagement; still, contrasting with prior work, we find no evidence that it is driven specifically by moral-emotional words as opposed to the more general and larger lexicon of moral language. Second, we identify a striking reversal in the relationship between moral language and engagement, a phenomenon we call “moral penalty.” We find that as the ratio of moral to non-moral words surpasses a threshold, the process of engagement around a post reverses with marked decreases in engagement and online diffusion. These findings help clarify links between morality and online engagement and contagion: infusing messages with moral language increases their spread to a point after which embedding posts in morality may backfire.
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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 | 0.002 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".