# The Detection of Fake News in Arabic Tweets Using Deep Learning
## 2 Related Work  
This section discusses the most recent related work to detect fake news using machine learning and deep learning. Detecting fake news in Arabic is still in its infancy compared with other languages, such as English. The models are discussed below and summarized in Table 1.  
Detection models based on news content features make up the majority of current studies in news truth verification. [PERSON] et al. [10] developed an approach using two CNN models to assess Arabic weblog posts' credibility. Both models have similar layers, except the embedding layer. The first model used pre-trained word-level embeddings, while the second used character-level embeddings. Each model was trained on a labelled dataset in the first iteration, and then the predictions of unlabelled data for each model were picked to re-train the other model. In the experiment, they compared the proposed model to a support vector machine (SVM) trained with a TF-IDF feature representation, CNN-trained with character-level vector representation, CNN-trained with word-level vector representation, and a combined model based on Word-CNN and Char-CNN. Their proposed model scored the highest F1-score of 0.63. The fundamental limitation of this work is that the amount of labelled data is small. The previous study [10] focused on examining the credibility of news blogs. Here, some works [11; 12; 13] assessed news verification models using fabricated news generated from real news stories by modifying their semantics. [PERSON] [11] proposed a system for claim verification based on the textual information of news. This work introduced a publicly available Arabic News Stance (ANS) dataset to determine the claims' veracity. The author acquired a subset of news titles from the Arabic news texts (ANT) dataset. The news titles were modified to generate fake claims. Two approaches have been used to train and test a generated dataset for claim classification: long short-term memory (LSTM) and pre-trained BERT model. This study reported that LSTM achieved the highest result for false claims recognition with an F1-score of 0.643. [PERSON] et al. [12] developed a method for automatically manipulating real multi-topic news to generate a fake news dataset, AraNews. They used transformer-based pre-trained models to detect manipulated Arabic news. Furthermore, they experimented with various modelling settings to examine the impact of their generated data on fake news verification models compared to a human-created fake news dataset. The authors reported that automatically generated news positively affects the fake news detection task. Compared to previous work [11], it achieved a better improvement with an F1-score of 0.0576. The ANS and AraNews datasets were utilized by another work [14]. This work intended to examine the performance of language models such as AraBERT, QARiB, and AraGPT2 when applied to the Arabic fake news detection task. Each model was trained and evaluated using the ANT and AraNews datasets. The results showed that AraBERT and QARiB revealed some ability to identify false news with a similar accuracy of 0.80. In both experiments, AraGPT2 achieved the lowest accuracy. [PERSON] et al. [13] proposed a machine learning model to assess the veracity of Arabic news articles. They gathered factual news articles related to a single domain, which is the Haji. After this, the acquired dataset was utilized to construct fake news articles relying on crowdsourcing. They extracted a set of linguistic features, including emotional, syntactical, polarity, and part of speech. The extracted features were used to train three classifiers, Naive Bayes (NB), Random Forest (RF), and SVM, to detect Arabic false news. The resultsdemonstrated that the extracted linguistic features could effectively detect fake news, and the best classifier was RF, which had a 0.79 accuracy rate. Another approach exploited textual features defined in [15] to automatically identified satire news. They released a dataset that was collected from a variety of news websites.