An Improved Fake News Detection Model Using Hybrid Time Frequency-Inverse Document Frequency for Feature Extraction and AdaBoost Ensemble Model as a Classifier

Abstract

Fake news information on the internet has recently emerged as one of the challenging concerns that can have an impact on society and individuals. Furthermore, the propagation of fake news on social media increases the risk of loss of trustworthiness and disseminates fake information across multiple online platforms. As a result, recognizing fake news on the internet plays an important role in society and among individuals. Knowing this, this study proposed an improved fake news detection model that uses the proposed Hybrid Time-Frequency-Inverse Document Frequency (TF-IDF) to extract features and the Adaptive boosting ensemble classifier, which is a combination of Iterative Dichotomiser 3 (ID-3), Random Forest (RF), and Nave Bayes (NB) classifiers. The characteristics are chosen using the Least Absolute Shrinkage and Selection Operator (LASSO), and the news is classified as fake or true using the Adaptive Boosting (AdaBoost) ensemble classifier. The obtained results show that TF-IDF with AdaBoost ensemble classifier has a higher classification accuracy of 98.98% than the existing N-Gram with TF-IDF and Bidirectional Encoder Representation from Transformer (BERT) and Word2Vec with Convolutional Neural Network-Bidirectional Long Short Term Memory (CNN-Bi LSTM) with 96.81% and 97.74%, respectively.