A machine learning approach to detecting fraudulent job types

Abstract

Job seekers find themselves increasingly duped and misled by fraudulent job advertisements, posing a threat to their privacy, security and well-being. There is a clear need for solutions that can protect innocent job seekers. Existing approaches to detecting fraudulent jobs do not scale well, function like a black-box, and lack interpretability, which is essential to guide applicants’ decision-making. Moreover, commonly used lexical features may be insufficient as the representation does not capture contextual semantics of the underlying document. Hence, this paper explores to what extent different categorizations of fraudulent jobs can be classified. In addition, this paper seeks to find what type of features are most relevant in classifying the type of fraudulent job. In this paper, we develop and validate a machine learning system for identifying identity theft, corporate identity theft and multi-level marketing amongst fraudulent job advertisements. We utilized four classes of features: empirical rule set-based features, bag-of-word models, most recent state-of-the-art word embeddings and transformer models for various machine learning classifiers. The machine learning models were validated by evaluating them on a publicly available job description dataset. Our results indicate that the word embeddings and transformer-based features consistently outperformed the handcrafted rule-set based features class. Ultimately, a Gradient Boosting classifier with a combination of empirical rule-set based features, parts-of-speech tags and bag-of-words vectors achieved the best performance with an F1-score of 0.88.

Pothole detection for autonomous vehicles using deep learning: a robust and efficient solution

“…The F1 score includes the harmonic mean of precision and recall, and its value ranges from 0 to 1, with 1 being the best possible score (El-Hasnony et al, 2022). It balances the precision and recall value, providing a single value that considers both false positives and false negatives (Naudé et al, 2023). This is particularly important in scenarios like pothole detection for autonomous vehicles, where correctly identifying potholes is crucial for safety, and false negatives (missing actual potholes) can have significant consequences (Naudé et al, 2023).…”

“…It balances the precision and recall value, providing a single value that considers both false positives and false negatives (Naudé et al, 2023). This is particularly important in scenarios like pothole detection for autonomous vehicles, where correctly identifying potholes is crucial for safety, and false negatives (missing actual potholes) can have significant consequences (Naudé et al, 2023). In pothole detection for autonomous vehicles using YOLOV8, the F1 score becomes a critical metric for model performance evaluation (Bosurgi et al, 2022).…”

Optimization of SVM and Gradient Boosting Models Using GridSearchCV in Detecting Fake Job Postings

“…The research found that the organizational type of feature is the best feature in detecting recruitment fraud as an independent model. Research conducted by Naude et al also focuses on detecting fake job recruitment types using the Gradient Boosting algorithm and using the steamy empirical rule set feature part-of-speech tags and bag-of-words vectors [18]. The research succeeded in achieving an F1 score of 88%.…”