Smart Fraud Detection Framework for Job Recruitments

How to detect propaganda from social media? Exploitation of semantic and fine-tuned language models

Online propaganda is a mechanism to influence the opinions of social media users. It is a growing menace to public health, democratic institutions, and public society. The present study proposes a propaganda detection framework as a binary classification model based on a news repository. Several feature models are explored to develop a robust model such as part-of-speech, LIWC, word uni-gram, Embeddings from Language Models (ELMo), FastText, word2vec, latent semantic analysis (LSA), and char tri-gram feature models. Moreover, fine-tuning of the BERT is also performed. Three oversampling methods are investigated to handle the imbalance status of the Qprop dataset. SMOTE Edited Nearest Neighbors (ENN) presented the best results. The fine-tuning of BERT revealed that the BERT-320 sequence length is the best model. As a standalone model, the char tri-gram presented superior performance as compared to other features. The robust performance is observed against the combination of char tri-gram + BERT and char tri-gram + word2vec and they outperformed the two state-of-the-art baselines. In contrast to prior approaches, the addition of feature selection further improves the performance and achieved more than 97.60% recall, f1-score, and AUC on the dev and test part of the dataset. The findings of the present study can be used to organize news articles for various public news websites.

A smart secured framework for detecting and averting online recruitment fraud using ensemble machine learning techniques

With the rise of the Internet and social media, information has become available at our fingertips. However, on the dark side, these advancements have opened doors for fraudsters. Online recruitment fraud (ORF) is one of the problems created by these modern technologies, as hundreds of thousands of applicants are victimized every year globally. Fraudsters advertise bogus jobs on online platforms and target job hunters with fake offerings such as huge salaries and desirable geographical locations. The objective of these fraudsters is to collect personal information to be misused in the future, leading to the loss of applicants' privacy. To prevent such situations, there is a need for an automatic detecting system that can distinguish between real and fake job advertisements and preserve the applicants' privacy. This study attempts to build a smart secured framework for detecting and preventing ORF using ensemble machine learning (ML) techniques. In this regard, four ensemble methods-AdaBoost (AB), Xtreme Gradient Boost (XGB), Voting, and Random Forest (RF)-are used to build a detection framework. The dataset used was pre-processed using several methods for cleaning and denoising in order to achieve better outcomes. The performance evaluation measures of the applied methods were accuracy, precision, sensitivity, F-measure, and ROC curves. According to these measures, AB performed best, followed by XGB, voting, and RF. In the proposed framework, AB achieved a high accuracy of 98.374%, showing its reliability for detecting and preventing ORF. The results of AB were compared to existing methods in the literature validating the reliability of the model to be significantly used for detecting ORF.

A machine learning approach to detecting fraudulent job types

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.