## 2 Background and Literature Survey
### Deep Learning and Transformer Learning for Deepfake News Detection  
Detecting fake news has grown increasingly important, as the internet is needed by the majority of people in every country on our planet. A study by [PERSON] et al. [16] and [PERSON] et al. [17] found that social media sites such as Facebook, Twitter, and WhatsApp are primarily used to disseminate fake news.  
Fake news is generally detected by classifying the sample from many areas, such as politics, entertainment, and so forth. Once this is accomplished, generalized machine learning models can be deployed. [PERSON] et al. [18] have studied the accuracy of multiple models employing textual and visual characteristic combinations extensively in the same way. [PERSON] et al. [19] proposed that textual traits and metadata be combined with the convolutional neural networks (CNNs) technique. To forecast the results of the provided samples, a bidirectional LSTM model with a fully connected layer was used, and SoftMax was used as an output. The highest accuracy of 27.7% was reached by focusing on political data with better input features. [PERSON] et al. [20] developed an improved technique forposture identification with four levels: agree, disagree, discuss, or unrelated, with an accuracy rate of 88.46%. To classify data, we employed the author's proposed model, which incorporates linguistic features for text processing and tokenization.  
[PERSON] et al. [21] proposed a machine learning (ML) model that utilized distinctive word embedding along with n-gram traits for posture identification in fake articles. Recurrent neural networks were utilized by [PERSON] et al. [22] in a hybrid deep learning model to classify bogus news with an accuracy of about 89.2%. When compared to the current models, the [PERSON] et al. [23] transformer model for BERT has an increase in the F1-score of 0.14. [PERSON] et al. [24] used only one dataset from Kaggle, which was employed in the BERT and bidirectional LSTM models, and achieved an F1 score of 0.98. The conventional ML algorithms [25], namely SVM and the fed Linguistic Analysis and Word Count (LIWC), exhibited an accuracy of 87%. However, [PERSON] et al. [26] introduced the fake BERT model, which has an accuracy of roughly 98.9% using only one dataset. [PERSON] et al. [27] aggregated three publicly available datasets and found that the combined dataset had an accuracy of 91%. By fine-tuning the BERT uncased pre-trained classifier, [PERSON] et al. [28] developed a universal model for spam detection that could be used to identify spam in any dataset. The final model was trained using all of the samples' hyperparameters, which were gathered from individual models that performed satisfactorily. According to the recommended finished model, the accuracy was around 97%.  
In conclusion, fake news can be identified from social media contexts, malicious user profiles, and user activities. However, despite their benefits, these approaches present several challenges as well [29]. For instance, it is difficult to enumerate social contexts since that is a broad field. Furthermore, detection algorithms may need to be more effective due to large amounts of data and their incompleteness, noise, and unstructured nature.  
The following list of limitations is observed over the course of the literature survey:  
* Detecting deepfake news is a challenge because of the lack of a benchmarked, labeled dataset with actual truth labels and a complete information space.
* False news has become increasingly widespread and difficult to detect in today's environment.
* The most challenging aspect of spotting fake news is doing it early. Lack of data to train detection models is another issue with detecting fake news.
* To identify false news, it is necessary to have a solid awareness of specific authors, entities, and the relationship that exists between each word in a lengthy text.
* To overcome the above-mentioned issues, we proposed and implemented a transfer learning-based multiplicative vector fusion (TL-MVF) model.