## 2 Methods
### Related Studies  
[PERSON] et al. [29] developed a new n-gram model to automatically identify false content with a clear emphasis on fake comments and fake news. They implemented TF-IDF for the extraction of features and six methods for classifying machine learning: SVM, Linear Support Vector Machine (LSVM), K-nearest neighbors (KNN), Decision Tree (DT), Stochastic Gradient Descent (SGD), and LR. Their experiments demonstrate very promising and enhanced results compared with traditional approaches. The best result is LSVM at 90% accuracy. [PERSON] et al. [30] presented various types of news story features, including source and social media posts. They demonstrate a new set of features and measure the predictive performance of KNN, NB, RF, SVM, and XGBoost (XGB) to automatically detect fake news. The best model is XGB with 86% accuracy. [PERSON] et al. [31] investigated the problem of rumor detection by exploring different models of deep learning with a focus on contextual consideration. The proposed system is bidirectional long-dependent short-term memory via a convolutionary neural network. Their experiments essentially classified tweets into rumors and nonrumors. Experimental results show that with 86.12% accuracy, the proposed method outperformed the baseline method and was more successful than the comparable approaches. [PERSON] et al. [32] proposed a convolutional neural network (CNN) with margin loss and several embedding models for detecting false news. Static word embeddings are compared to nonstatic word embeddings, which allow for gradual uptraining and updating of word embeddings during the training process. Two recent well-known datasets in the field, ISOT and LIAR, are used to test their suggested designs. The results on the optimal architecture indicate promising results, exceeding state-of-the-art approaches by 7.9% on ISOT and 2.1% on the LIAR dataset test set. [PERSON] et al. [33] present an ensemble classification model for detecting false news that outperforms the current state-of-the-art models in terms of accuracy. The proposed technique collects key features from false news datasets, which are then identified using an ensemble model that combines three common machine learning models: decision tree, random forest, and extra tree classifier. On the Liar dataset, they achieved training and testing accuracies of 99.8% and 44.15%, respectively. Additionally, they were able to obtain 100% training and testing accuracy with the ISOT dataset. [PERSON] et al. [34] proposed a hybrid neural network design that combines the capabilities of CNN and LSTM, as well as two alternative dimensionality reduction algorithms--PCA and chi-square. Before providing the feature vectors to the classifier, this work advocated using dimensionality reduction techniques to minimize their dimensionality. This research used a dataset from the Fake News Challenges (FNC) website to create the logic. The dataset contains four sorts of stances: agree, disagree, discuss, and unrelated. PCA and chi-square are fed the nonlinear features, which provide more contextual features for fake news detection. The goal of this study was to determine what the position of a news article is in relation to its headline. In terms of accuracy and F1 score, the suggested model increases the results by 4% and 20%, respectively. The experimental results reveal that PCA surpasses chi-square and other state-of-the-art approaches by 97.8%. [PERSON] et al. [35] applied artificial intelligence and machine learning to develop automatic detection algorithms. The recent breakthroughs in deep learning techniques in complicated natural language processing tasks make them a plausible answer for detecting fake news as well. For false news classification, the paper introduces a novel hybrid deep learning model that blends convolutional and recurrent neural networks. On two fake news datasets (ISO and FA-KES), the model was effectively verified, yielding detection results that were much superior to nonhybrid baseline approaches.