Social identity shapes antecedents and functional outcomes of moral emotion expression in online networks
Bibliographic record
Abstract
There is increasing evidence that moral and emotional rhetoric spreads widely on social media and is associated with intergroup conflict, polarization, and the spread of misinformation. However, this literature is largely correlational, making it unclear why moral and emotional content drives sharing and conflict. In this research, we examine the causal impact of moral-emotional content on sharing decisions and examining how social identity shapes the antecedents and functional outcomes of decisions to share. Across five pre-registered experiments (N = 2,498), we find robust evidence that the inclusion of moral-emotional expressions in political messages causes intentions to share the messages on social media. Moreover, individual differences in the strength of partisan identification and ideological extremity are robust predictors of sharing messages with moral-emotional expressions, even when accounting for attitude strength. But we only found mixed evidence that brief manipulations of identity salience increased sharing. In terms of functional outcomes, when partisans choose to share messages with moral-emotional language, people perceive them as more strongly identified among their partisan ingroup, but less open-minded and less worthy of conversation with outgroup members. These experiments highlight the causal role of moral-emotional expression in online sharing intentions, and how such expressions in online networks can serve ingroup reputation functions while hindering discourse between political groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".