## 2 Literature Review  
Disinformation is difficult to detect because it is purposefully written to misrepresent information. The major barrier to accurately identifying fake news is the scarcity of human news labeling. Numerous research studies already exist on false news detection utilizing various classification methods. It is critical to pick the proper feature reduction algorithm, as feature reduction has a significant impact on text classification performance [36]. For this reason, traditional machine learning-based methods perform poorly on high-dimensional features [37]. For the most part, neural network-based approaches have performed better than traditional machine learning-based methods, as they reduce the curse of dimensionality. Several studies have used CNN for extracting useful features, and long short-term memory (LSTM) has been utilized for classification [13; 21]. A neural network's performance is significantly influenced by hyperparameter optimization. [PERSON] et al. [19] proposed an optimized CNN (OPCNN-FAKE) model to detect fake news. Their findings indicated that CNN with optimized parameters outperformed the baseline models when they compared their model to RNN and ML classifiers. Attention techniques have recently been used to efficiently obtain information from news content associated with a mini query [38; 39]. A study by [PERSON] et al. [38] utilized a three-level hierarchical attention network, 3 HAN, which has three levels: one for words, one for sentences, and one for the headline. 3 HAN can allocate weights to distinct sections of an article thanks to its multiple levels of attention. In comparison to other deep learning models, 3 HAN produces more accurate results despite the fact that it only uses textual data. However, the studies mentioned earlier that employ neural networks mainly use non-contextual embedding, which does not consider the contextual meaning of the text, and has thus yielded minimal success. Furthermore, the majority of models fail to achieve satisfactory detection performance. To compensate for the shortcomings of previous works, [PERSON] [40] suggested self-attention-based Automatic Fake News Classification (ACT). Their principle is derived from the fact that texts are typically short and thus inefficient for classification. Their proposed framework employs mutual interactions between a claim and numerous supporting replies. While the article input is fed into the LSTM, the final step of the LSTM output does not always entirely reflect the semantics of the article. By connecting all word vectors in the text, a massive vector dimension can be obtained. Thus, the internal correlation between the words in the articles can be overlooked. As a consequence of using the self-attention process with the LSTM model, essential components of the article are extracted using several feature vectors. Their approach is highly dependent on self-attention and a representation matrix of articles. Graph-aware Co-Attention Networks (GCAN) is a relatively new method to detect fake news [41]. GCAN can decide whether a short-text Twitter post is fraudulent based on the historical record of its retweeters. This model, however, is not appropriate for long texts. Another notable advancement in the NLP field is language models, which produce contextual embeddings. Bidirectional Encoder Representation (BERT) is mainly used in fake news detection tasks. [PERSON] et al. [26] included news data in BERT's pre-training phase to improve fake news detection performance. ExBAKE (BERT with additional unlabeled news text data) outperformed the state-of-the-art model stackLSTM by a 0.137 F1 score. [PERSON] et al. [42] demonstrated a BERT-based Domain-Adaption Neural Network (BDANN) for multimodal fake news detection. The significant parts of BDANN are a multimodal feature extractor, a false news detector, and a domain classifier. The pre-trained BERT method was utilized to extract text features, while the pre-trained VGG-19 (a 19-layer deep CNN) model was used to extract visual features.