# The Detection of Fake News in Arabic Tweets Using Deep Learning
## 2 Related Work
They analysed the linguistic properties of news and concluded that false news involves highly positive and negative keywords and tends to be written in a more subjective tone. Machine learning and deep learning models have been trained to identify satirical news. A CNN with pre-trained word embeddings achieved the highest performance with an accuracy of 0.98. During the COVID-19 pandemic, a lot of false information was disseminated through various social networking applications. The effect of misinformation is not confined to individual lives but also includes society and the economy. Several studies [3; 6; 14; 16; 17; 18] have concentrated on assessing the credibility of information related to the spread of COVID-19 in Arabic communities via social media platforms such as Twitter. [PERSON] et al. [3] constructed a large COVID-19 dataset and made it available to the public. The dataset is split up into two sets. The first set is used to construct two Arabic word embedding models based on the word2 vec and fastText tools. Two Arabic native speakers volunteered and annotated another set manually. Then, they assembled a feature vector for each Tweet using TF-IDF and word embedding models. Machine learning and deep learning models have been implemented to discover misinformation. Generally, all models' accuracy improved when pre-trained word embedding models were utilized as feature extractors. They reported that the XGBoost classifier outperformed all other models. The AUC optimization technique was used to deal with the issue of imbalanced training data. Unfortunately, it failed to improve the performance of all classifiers. [PERSON] and [PERSON] [6] claimed that the performance of the fake news detection model is strongly correlated with the dataset size that the model will be trained on. Therefore, they created a large manually labelled dataset of fake Arabic news regarding COVID-19. To assess the accuracy of their dataset, they used it to train and evaluate three pre-trained transformer-based models as baseline models. Additionally, two transformer models were further pre-trained using COVID-19 data. Experimental results demonstrated that the additional training improved the results compared to baseline models for fake news verification. [PERSON] et al. [14] conducted a comprehensive work investigating several neural network and language models for recognizing and eliminating fake Arabic news on social networks. The models were trained and evaluated on ArCOV19-Rumors and tested on COVID-19-Fakes. The findings showed that transformer-based models outperformed the neural network-based approaches in classifying COVID-19 news as fake or real. QARiB achieved the best result with a reported accuracy of 0.95. Nevertheless, low accuracy was achieved by QARiB when generalized on a new dataset. [PERSON] and [PERSON] [16] suggested an automatic system for identifying false Arabic news relating to the COVID-19 crisis on Twitter. They manually and automatically constructed annotated datasets. Four phases were performed to prepare the Arabic datasets associated with COVID-19. Firstly, they collected 5.5 million Tweets using a collection of hashtags that existed during the COVID-19 pandemic. Secondly, they used two fact-checkers to generate a list of terms relevant to COVID-19 rumours. Thirdly, they only returned Tweets containing at least one of these keywords. Lastly, a small sample of Tweets was manually annotated. For the remaining Tweets, they selected the best-performing classifier trained on the manually annotated dataset to classify unlabelled Tweets as fake or real automatically. Their experiment employed various supervised machine learning classifiers to train and validate it on manually and automatically annotated datasets. They used four feature extraction techniques and stemming and rooting techniques to perform further pre-processing on collected Tweets.