LITERATURE REVIEW
In this study, Hakak et al. identify the most significant characteristics that influence fake news classification. Ensemble models are used to achieve optimal accuracy in classifying fake news datasets. The ensemble classifier requires less training time. From the fake news datasets, the proposed model extracts significant features and then classifies the extracted features using a hybrid ensemble model consisting of Decision Tree, Random Forest and Extra Tree Classifier. A training accuracy of 99.8% and a testing accuracy of 44.15 percent were achieved on the Liar dataset. Training and testing accuracy were both 100% in the ISOT dataset. In its paper, Hossain et al. discuss the fact that feature engineering can be used to effectively address this issue: limiting the spread of fake news at the source, not after it has become a global problem. We manipulated text with extracted features, resulting in an effective level of fake news detection.
A deep learning model was then developed and tested. Using this study, it was successfully shown that the original features had an impact on deep learning models that were unknown.
By analyzing the accuracy of a report and predicting its authenticity, we propose a model for detecting fake news (Agarwal & Dixit, 2020). By extracting features from the textual information and constructing credibility scores, this model builds an ensemble network that can simultaneously learn the depictions of news reports, authors, and titles. A variety of machine learning algorithms are used for higher accuracy, including SVM, CNN, LSTM, KNN, and Naive Bayes, and the LSTM algorithm shows the best accuracy at 97%. Using precision, recall, and the F1-Score, we evaluated the performance and effectiveness of classifiers. Different algorithms were used to show the effectiveness of the performance. Kaliyar et al., 2019), In this work, the author proposes an ensemble machine learning framework based on a tree-based gradient boosting technique, which combines content characteristics and contextual features for the detection of fake news. Recently, gradient descent algorithms have been derived as adaptive boosting methods for classification problems. A single objective function is optimized using this formulation, which shows why specific elements and parameters are used in the methods. We apply various machine learning models for classification based on a multi-class dataset (FNC). Comparing the ensemble framework to existing benchmark results, experimental results demonstrate its effectiveness. For multi-class classification of fake news with four classes, we achieved an accuracy of 86% by using Gradient Boosting algorithm (an ensemble machine learning framework). (Elyassami et al., 2022) classifies news as fake or real by using machine learning models. A total of five classifiers were developed using Random Forest, Support Vector Machine, Gradient Boosting, Logistic Regression, and Naive Bayes. Open-source datasets extracted from online sources covering a variety of domains were used to train the models. Using text lemmatization, vectorization, and tokenization, valuable information was extracted from news text, increasing generalization and accuracy of fake news classification models. An investigation of how voting strategy impacts ensemble learning models was conducted. The four performance measures used to evaluate the five classifiers were accuracy, F1-score, recall, and precision. We are encouraged by the results. It is possible to use these ensembles against fake news spreading since they outperform other classifiers when trained with random forest algorithms and gradient boosting algorithms. In this paper, (Masciari et al). present their complete framework for detecting fake news and describe their machine-learning-based solution. Using two well-known and widely used real-world datasets, we demonstrate that our settings are superior to state-of-the-art algorithms and are capable of detecting fake news with high accuracy even in the absence of complete content information.