Artificial intelligence (AI)-based cybersecurity with a focus on social media fake news detection

Abstract

Ever since the explosion of the internet, fake news has always been a cause for concern. The proliferation of fake news online hinders access to reliable information. The efficiency of several machine learning methods for the identification of fake news is investigated in this work. We train and evaluate five models: Support Vector Machine (SVM), Logistic Regression, Random Forest, Long Short-Term Memory (LSTM), and Naive Bayes. Employing two distinct datasets, we evaluate the models' generalizability. We extract textual features from the news articles and assess their performance using established metrics. This investigation sheds light on the advantages and limitations of each model within the context of fake news classification, contributing to the development of more robust detection systems. Furthermore, we explore the impact of utilizing different machine learning paradigms, including supervised learning (Logistic Regression, Random Forest, SVM) and deep learning (LSTM) on the detection accuracy. This comparative analysis provides valuable insights into the optimal approach for tackling the intricate challenge of fake news identification.