The devil is in the details! Effect of differentiated platform governance on online review manipulation
Bibliographic record
Abstract
Recent years have witnessed an increasing number of manipulated online reviews in e-commerce platforms. Previous research has provided substantial evidence that vendor manipulation of online reviews has a significant negative impact on the stakeholders involved in the e-commerce business. Many platforms take various governance measures to filter manipulated reviews. Nevertheless, the effectiveness of these measures still remains unknown to a large extent. To bridge this research gap, this paper investigates the effect of differentiated platform governance, including defined as interventions to counterattack manipulation intensity, manipulation duration, and perceived quality manipulated, on the probability of future review manipulation. We develop a game theoretical model that incorporates the strategic interactions between the platform and vendors, which yield several testable hypotheses. We then conduct an empirical analysis of platform governance and review manipulation by using the review manipulation data collected from Amazon.com. Results of the analytical model and empirical analysis show that platform governance that targets manipulation intensity and manipulation duration can both effectively mitigate review manipulation probability. On the contrary, platform governance to counterattack manipulating perceived product quality exhibits an inverted U-shape relationship with review manipulation probability. This study provides novel insights into how to better mitigate online review manipulation for e-commerce platforms.
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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 | 0.019 | 0.157 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".