An Intelligent Framework for Deceptive Review Detection Using Advanced Trust Vector Modeling
Bibliographic record
Abstract
Online review platforms have drastically reshaped how we interact, make purchasing decisions, and engage with digital content. However, the rise of deceptive content, privacy breaches, and misinformation has undermined online content credibility, impacting the trustworthiness of the information shared. To address these issues, we propose a robust framework for automated trust assessment of online reviews, focusing on identifying deceptive online content. The core of our approach is the trust vector, a novel feature representation that captures key user engagement factors influencing content trustworthiness. By applying the Weighted Trust Scoring Method (WTSM), we calculate a weighted trust score that strengthens the model’s interpretability and effectiveness in trust evaluation. The proposed model is evaluated on two benchmark datasets-the Deceptive Opinion Corpus Dataset and the Yelp Review Dataset. The framework achieves classification accuracies of $85 \%$ and $87 \%$, respectively, demonstrating its effectiveness in distinguishing deceptive from truthful content.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".