Detecting Fake News Over Job Posts via Bi-Directional Long Short-Term Memory (BIDLSTM)

T V Divya,Barnali Gupta Banik

doi:10.4018/ijwltt.287096

Abstract

Fake news detection on job advertisements has grabbed the attention of many researchers over past decade. Various classifiers such as Support Vector Machine (SVM), XGBoost Classifier and Random Forest (RF) methods are greatly utilized for fake and real news detection pertaining to job advertisement posts in social media. Bi-Directional Long Short-Term Memory (Bi-LSTM) classifier is greatly utilized for learning word representations in lower-dimensional vector space and learning significant words word embedding or terms revealed through Word embedding algorithm. The fake news detection is greatly achieved along with real news on job post from online social media is achieved by Bi-LSTM classifier and thereby evaluating corresponding performance. The performance metrics such as Precision, Recall, F1-score, and Accuracy are assessed for effectiveness by fraudulency based on job posts. The outcome infers the effectiveness and prominence of features for detecting false news. .

Highlights

As stated by the United States (US) Department of Labor, the rate of unemployment is 11.1% in the Bureau of Labor Statistics US Department of Labor, Employment Situation of US as of June 2020., Even though a lot of factors exist behind the present unemployment rate, several people are there in the US as well as in other parts of the world, those who look forward to getting new jobs because of the job loss and some other financial crisis
The graphs compare the weighted average results of precision, recall, and F1-score obtained by the proposed Bi-Directional Long Short Term Memory (Bi-Long Short-Term Memory (LSTM)) classifier and prevailing Logistic Regression (LR), Artificial Neural Network (ANN), support vector machine (SVM), K Nearest Neighbor (KNN), random forest classifier (RF), and XGBoostclassifiers
WORK DIRECTION This study proposes the classifier called Bi-Directional Long Short Term Memory (Bi-LSTM) for the initial stage detection of job-associated fake news that has been posted over the social media

Summary

Introduction

Volume 16 Issue 6 in job boards directly or pulling job data from the job aggregators, since most of the job postings have been performed online. It is unsure that every job postings are real, since some of them are fraudulent that may be intended to obtain data or other confidential details from desperate job seekers. It has been posted in USC Career Center.

Methods

Results

Conclusion