Vulnerabilities to Crypto Currency Scams and Online Persuasion Strategies
Bibliographic record
Abstract
As deepfakes and scams online become more common, many individuals, organizations and nation-states struggle to maintain trust and remain credible sources for their stakeholders. Increasingly algorithms shape the digital information landscape, choosing what content is displayed and deepening the individual silos of information seeking. Recently it has been suggested that the best efforts to combat misinformation are not to try to stop its spread but through understanding the vulnerabilities on which it lands in the individual receiving the false information. There is an urgent need to investigate the mechanisms and extent of deception in online environments, as little is known about these specific vulnerabilities that then cause individuals to become victims for online scams. In the digital environment, different vulnerabilities exist yet they result from siloed studies in specific contexts. This paper starts by categorizing the different levels on which digital communication may be vulnerable. Further, this research asks how these vulnerabilities are utilized and what persuasion tactics are at use when crypto scams are concerned. Building on the persuasion principles, this paper analyzes three recent highly successful online scams. The findings conclude that social proof and scarcity were most used influence mechanisms, suggesting that scam prevention needs to understanding the vulnerabilities on which these influence mechanisms build.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".