Analysis of User-Generated Content to Unfold Privacy Concerns with CBDCs
Bibliographic record
Abstract
The interest in central bank digital currencies (CBDCs) has motivated several initiatives around the world led by central banks. As the success of CBDCs depends on the public trust and use, it is crucial to understand the privacy concerns of users towards CBDCs as a significant determinant of their adoption and usage behavior. This paper introduces a novel datadriven approach utilizing user-generated content (UGC) from Reddit and aspect-based sentiment analysis (ABSA) to assess dimensions of user privacy concerns and prioritize the specific issues contributing to overall privacy concerns regarding CBDCs. By integrating the concern for information privacy (CFIP) and the mobile users’ information privacy concerns (MUIPC) frameworks, this study highlights the key areas and concerns that need to be addressed to ensure a successful integration of CBDCs in the existing financial system. The findings reveal that perceived surveillance, perceived intrusion, concern of collection, improper access, unauthorized secondary use, and errors, all significantly contribute to the overall privacy concerns of users. The paper proposes a novel, objective approach to measure privacy concerns constructs using the content posted on social media and demonstrates its effectiveness to address typical limitations of survey-based studies, including small sample sizes and generalizability issues. Index Terms-Central bank digital currency (CBDC), Privacy concerns, Aspect-based sentiment analysis (ABSA), Concern for information privacy (CFIP), Mobile users’ information privacy concerns (MUIPC).
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".