The moral foundations of cryptocurrency: evidence from Twitter and survey research
Bibliographic record
Abstract
Despite its relatively brief history, cryptocurrency has already had a profound impact on the economy, with some predicting that it will eventually replace traditional fiat currencies. Historically, it had dark associations with illegal activities in the early days, although perceptions and associations likely have, in recent years, changed for the better. Thus, understanding how people perceive the morality of cryptocurrency currently forms the motivation of the current research. We, in particular, examine associations dependent on political ideology. Across both a large-scale analysis of Twitter posts ( N = 959,393) and controlled survey research ( N = 487), we find that cryptocurrency is currently best understood as being more strongly linked to conservative vs. liberal moral foundations. Cryptocurrency-related posts were more likely to express conservative moral foundations (Authority, Purity, and Loyalty) rather than liberal moral foundations (Fairness and Care), and individual endorsement of these conservative moral foundations was associated with increased interest in crypto investment.
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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 | 0.005 | 0.003 |
| 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 | 0.001 |
| 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".