Estimating the effect size of moral contagion in online networks: A pre-registered replication and meta-analysis
Bibliographic record
Abstract
Abstract Over 5 billion people now use social media platforms. As our social lives become increasingly entangled with online social networks, it is important to understand the dynamics of online information diffusion. This is particularly true for the political domain, as political elites, disinformation profiteers, and social activists all use social media to gain influence by spreading information. Recent work found that emotional expressions related to morality (moral-emotion expression) are associated with increased diffusion of political messages—a phenomenon we called “moral contagion.” Here, we perform a large, pre-registered direct replication (N = 849,266) of Brady et al. using the dictionary methods from the original paper, as well as new large-language models. We also conduct a meta-analysis of all available data testing moral contagion (5 labs, 27 studies, N = 4,821,006). The estimate of moral contagion in the available population is positive and significant (IRR = 1.13, 95% CI = [1.06, 1.20]), such that for each additional moral–emotional word in a post, the expected number of shares was 13% greater. The mean effect size of the pre-registered replication (IRR = 1.17) better estimated the population effect than the original study (IRR = 1.20). Contrary to prior work, we find that the moral contagion model substantially outperforms nonsense models of diffusion (“XYZ contagion model”). Moral contagion was also conceptually replicated when moral–emotional content was measured using state-of-the-art natural language processing methods. These findings reveal that the moral contagion effect is highly robust across datasets and methods.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".