Fake Job Detection using Bi-GRU Algorithms with Combination Random Over Sampler and Weighted Random Sampler
Bibliographic record
Abstract
In order to address data imbalance, this study created a model for identifying fraudulent job openings using the Bidirectional Gated Recurrent Unit (Bi-GRU) algorithm in conjunction with Random Over Sampler and Weighted Random Sampler. With 17.880 data points 95.16% actual and 4.84% false. The EMSCAD dataset was utilized, the model integrates numerical features (salary_range) that are standardized using StandarScaler, and text processing with GloVe embedding and attention technique. To address severe class imbalance, a mix of oversampling and weighted sampling approaches was used. This study use the Cross Industry Standard Process for Data Mining (CRISP-DM) method, The Bi-GRU model with ROS + WRS combination performed the best according to the trial results Accuracy 99.2%, Precision 91%, Recall 89.2%, F1-Score 90%, and ROC Curve 0.9909. This model outperforms other studies (Bi-LSTM 98.71%, XGBoost 97.8%, Random Forest 97%, Transformer XLNet model 83.9%), in identifying fraudulent job openings with an ideal balance between Precision and Recall demonstrating the efficacy of hybrid data balancing strategies. This study lowers financial losses from fraud, boosts user confidence in online recruitment platforms and offers a reliable detection method for production deployment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".