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Detection of fake news on job advertisements has grabbed the attention of many researchers over the past decade. Various forms of classifiers such as support vector machine (SVM), XGBoost classifier, and random forest classifier (RF) methods are greatly utilized for fake and real news detection about job advertisement posts in social media. There exhibits the slight or elusive variance among fake and the real news, which are obtained through topics and word embeddings, that affect system accuracy. Initially, pre-processing steps for job post data like stop word removal, tokenization, and lemmatizing words are done utilizing wordnet. The oversampling procedure is accomplished for data balancing. Subsequently, the new columns are generated representing each possible attribute value existence from original data by suggesting one-hot encoding. The dataset insignificant features removal is accomplished, which is exploited for fake news detection. As a final point, bi-directional long short-term memory classifier (Bi-LSTM) is greatly utilized for learning word representations in lower-dimensional vector space and learning significant words word embedding or terms revealed through the word embedding algorithm. The fake news detection is greatly achieved along with real news on job posts from online social media which is achieved by Bi-LSTM classifier, thereby evaluating corresponding performance. The performance level 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.

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