Deep Dive into Fake News Detection: Feature-Centric Classification with Ensemble and Deep Learning Methods

Abstract

The online spread of fake news on various platforms has emerged as a significant concern, posing threats to public opinion, political stability, and the dissemination of reliable information. Researchers have turned to advanced technologies, including machine learning (ML) and deep learning (DL) techniques, to detect and classify fake news to address this issue. This research study explores fake news classification using diverse ML and DL approaches. We utilized a well-known “Fake News” dataset sourced from Kaggle, encompassing a labelled news collection. We implemented diverse ML models, including multinomial naïve bayes (MNB), gaussian naïve bayes (GNB), Bernoulli naïve Bayes (BNB), logistic regression (LR), and passive aggressive classifier (PAC). Additionally, we explored DL models, such as long short-term memory (LSTM), convolutional neural networks (CNN), and CNN-LSTM. We compared the performance of these models based on key evaluation metrics, such as accuracy, precision, recall, and the F1 score. Additionally, we conducted cross-validation and hyperparameter tuning to ensure optimal performance. The results provide valuable insights into the strengths and weaknesses of each model in classifying fake news. We observed that DL models, particularly LSTM and CNN-LSTM, showed better performance compared to traditional ML models. These models achieved higher accuracy and demonstrated robustness in classification tasks. These findings emphasize the potential of DL models to tackle the spread of fake news effectively and highlight the importance of utilizing advanced techniques to address this challenging problem.

Numbers Do Not Lie: A Bibliometric Examination of Machine Learning Techniques in Fake News Research

“…Machine learning plays a crucial role in reducing the spread of fake news by using complex algorithms to examine huge volumes of textual data [12]. These algorithms, which have been trained on a variety of datasets, recognize language nuances and patterns that suggest false information, and their accuracy is improved by natural language processing techniques, which enable dynamic adaptability to changing disinformation strategies [13]. By empowering platforms and consumers to make informed and educated choices regarding the reliability of news sources, this technology helps to strengthen the fight against the spread of false information.…”

“…Those subjects have attracted the interest of many researchers that have conducted multiple investigations, such as, but not limited to, combating fake news with transformers [14], providing an automated classification of fake news spreaders for the purpose of breaking the misinformation chain [15], detecting fake news through the use of machine learning and deep learning [16], employing automatic fake news detection in the case of online news [17], using a feature-centric classification approach that integrates both ensemble learning and deep learning methods for fake news detection [13], and predicting the evolution of news spread [18] or broader subjects related to discussing the trends and challenges in identifying fake news on social networks based on natural language processing [19].…”

An efficient fake news classification model based on ensemble deep learning techniques

“…Low price, quick access, simplicity of use, and availability across all digital platforms, including computers, cellphones, iPods, and other devices, are crucial aspects in the market be unique. (1,2) Fake news implies spread of false information on social media with the intention of confusing or misinforming readers in order to further commercial or political objectives. (3) Additionally, the sector of news authoring and distribution is seeing an increase in a variety of players, which has produced news articles that are hard to determine whether they are legitimate or not.…”