The illusion of trust and the paradox of disclosure: how fake physician reviews exploit privacy concerns
Bibliographic record
Abstract
Purpose Online reviews shape consumer decisions, even in healthcare, a credence service where expertise is difficult to evaluate. Like unethical retailers, some healthcare providers post fake reviews. However, the impact of fake reviews on potential patients remains unclear. Using a dataset of fake reviews, this study examines how patients perceive the helpfulness and trustworthiness of fraudulent vs genuine physician reviews. Design/methodology/approach We used an archival dataset containing a representative sample of 5,000 online physician reviews, both fake and genuine, and performed empirical analysis. In addition to the helpful votes obtained from the data, we used large language models to derive the perceived trustworthiness score. Findings Fake physician reviews are paradoxically perceived as more helpful and trustworthy than genuine reviews. To unravel the underlying mechanism, we investigated the extent of personalized and specific health information. We found that fake reviews often contain more personalized and specific health information, making them appear more credible. Research limitations/implications Results may not generalize beyond online physician reviews. Future research could extend this investigation to other contexts. Practical implications Online platforms may need to reconsider their approach to managing online reviews, address ethical concerns, and strengthen regulatory oversight in sensitive areas, particularly in healthcare. Originality/value This study highlights an ethical paradox: while patients seek detailed health information, privacy concerns limit real patients from sharing such details, creating an information gap that fake reviews exploit. This is the first study to make use of unique data that contains real fake online physician reviews.
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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 | 0.028 | 0.222 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".