## 2 Related Work
### Machine Learning-Based Classification  
In a considerable amount of research, machine learning algorithms have been used for fake news detection. The credibility of fake news is one of the most important discussions, and many approaches have evolved with time for its detection. To detect fake news in online text, [PERSON] [26] et al. utilized deep learning algorithms, such as LSTMs and RNN. Models (vanilla and GRU) were implemented on the LIAR dataset. Among all algorithms, GRU showed the best performance, so in order to achieve better accuracy, a hybrid model was developed using the techniques of CNN and GRU on the dataset. For the detection of fake news, [PERSON] et al. and [PERSON] [27] applied different machine learning approaches. More machine learning techniques for the detection of fake news can be found in [28; 29; 30]. [PERSON] et al. [31] used a long short-term recurrent neural network and hybrid between convolutional neural network models. They implemented various deep neural networks: (1) LSTM, (2) LSTM along with dropout regularization, and (3) LSTM-CNN. Among all approaches, LSTM stands out and gives 82% accuracy. [PERSON] et al. [32] provided a model of decent accuracy to identify fake news using a framed model combined with knowledge engineering and machine learning. In another work, automated discovery of social news is proposed, utilizing three-element extraction procedures, a count vectorizer, term frequency-inverse document frequency, and a hashing vectorizer [4]. An ensemble-based technique for fake news detection is presented in [33]. Ensemble-based approaches combined various weak classifiers to achieve better accuracy for combined classification tasks. In [34], various machine learning algorithms, such as logistic regression, naive Bayes, and random forest classification, are used. In [31], a deep learning technique called Fake-BERT was used for the detection of fake news. In [6], a deep learning-based model, EchoFakeD, was proposed with a mix of content and contextual features. The authors proposed an effective tensor factorization scheme. In a number of studies, data augmentation, transfer learning, auto-encoders, and other semi-supervised models have been used for fake news detection [8]. A capsule-based neural network was used in [3] to classify fake news. In [35], the authors used geometric deep learning based techniques for fake news detection. These are an extension of the convolutional neural network that fuses other information, such as user profiles, news propagation, and the actual content. A hybrid deep learning model based on the combination of CNN and RNN was presented in [36]. The proposed model utilizes a combination of embedding, CNN, and RNN layers implemented in Keras and tested on ISO and FA-KES datasets. In [37], blockchain technology was used for the detection of fake news. In recent years, following the spread of COVID-19, several pieces of fake news have spread in this context. Therefore, numerous studies have focused on the detection of news related to COVID-19. For instance, a novel approach to the detection of fake tweets related to COVID-19 was proposed in [8]. In a similar direction, an analysis of public sentiments based on tweets related to COVID-19 was performed in [38]. In [36], several supervised learning approaches, such as CNN, LSTM, and BERT, were used for the detection of fake news related to COVID-19. Moreover, unsupervised learning techniques, such as model pre-training and distributed word representations, were used. After an extensive review of the literature, it was found that most of the studies on this topic have focused on stance detection, author credibility, and classification of news. However, existing approaches are limited because of the lack of social or political context awareness underlying the news. Therefore, a multi-stage pipeline is required for the correct classification of the credibility of news. This paper presents a novel approach, combining stance detection, author credibility, and news classification.