Automated Detection of Fake Job Postings Using Text Mining

Introduction

The proliferation of online job postings has led to an increase in fraudulent job advertisements, posing significant risks to job seekers. This project proposal aims to develop an automated system for detecting fake job postings using text mining techniques. The system is inspired by recent research that leverages machine learning models to identify fraudulent patterns in job descriptions.

Background

Recent studies have demonstrated the effectiveness of machine learning and natural language processing (NLP) in detecting fraudulent job advertisements. These techniques analyze both textual and numeric features of job postings to uncover patterns indicative of scams. Models such as Bidirectional Long Short-Term Memory (Bi-LSTM) have shown promising results in capturing the nuances of language used in fake ads.

Project Objective

The primary objective of this project is to create a robust automated detection system for fake job postings. The system will utilize advanced text mining techniques and machine learning models to differentiate between legitimate and fraudulent job ads, thereby enhancing the integrity of online job markets.

Methodology

1. Data Collection and Preprocessing

• Datasets: The project will use the "Real or Fake Job Posting Prediction" dataset available on Kaggle, which contains 17,880 labeled job postings with both genuine and fake entries.
• Text Preprocessing: Implement text cleaning techniques such as lemmatization, stop words removal, and punctuation removal to prepare the data for analysis.

2. Model Architecture

• Bi-LSTM Model: Develop a Bidirectional LSTM model to process sequential data effectively, capturing context from both directions in the text.
• Feature Engineering: Extract relevant features from job descriptions, such as word embeddings and syntactic patterns, to enhance model performance.

3. Training and Evaluation

• Training: Use supervised learning techniques to train the model on the labeled dataset.
• Evaluation Metrics: Evaluate model performance using metrics like accuracy, precision, recall, F1-score, and ROC AUC score.

Expected Outcomes

The proposed system is expected to achieve high accuracy in identifying fake job postings compared to traditional methods. By leveraging advanced text mining and machine learning techniques, the system should effectively handle diverse linguistic patterns and improve detection rates.

Conclusion

This project aims to contribute to the development of automated tools for detecting fake job postings, thereby safeguarding job seekers from potential scams. The integration of Bi-LSTM models with comprehensive feature extraction is anticipated to provide significant improvements in fraud detection capabilities.

For further details on related research, please refer to the paper "Automated Detection of Fake Job Postings Using Text Mining," available at sciencedirect.com/science/article/pii/S1877050917301813.

The dataset used for this project can be accessed at Kaggle - Real or Fake Job Posting Prediction.