# The Detection of Fake News in Arabic Tweets Using Deep Learning
## 2 Related Work
From the findings, they implemented the DT model in the last module for the classification task. [PERSON] and [PERSON] [20] started by collecting Tweets associated with real or fake news. They utilized Anti-Rumor's authority to obtain fake news. They aimed to construct a system able to detect rumours early and before anyone issues a clarification. Therefore, they removed any Tweet that contained a comment denying a rumour. A set of previously proposed features have been implemented for fake news classification, in addition to two new features suggested by them. The first feature investigates if the Tweets contain a link to sensitive content. The second feature checks if credible accounts follow the user. The k-best method has been used to assess whether features are useful for fake news detection. Generally, user-based features appeared more useful, and they achieved the best classification result with the expectation-maximization (EM) algorithm. Semi-supervised and unsupervised learning models are used to train their proposed model. The semi-supervised EM achieved a better result than NB when the training size exceeded 60%. Varied results of experimented with unsupervised EM. It is based on the initial value of the model parameters. The limitation of this work involved it relied on a single fact-checking site as a source of rumours and the unavailability of the dataset. [PERSON] and [PERSON] [21] introduced a machine learning model to evaluate the credibility of Arabic Tweets. This work focused on using the most effective content features as well as news publisher features that have been suggested and investigated in prior works. Moreover, a new feature was proposed by them. This feature measures the similarity score between the two Twitter user names (screen name and user name). The new feature did not significantly improve classification results based on the obtained results. They reported that RF performed the best, with an F1-score of 0.776. [PERSON] et al. [22] designed a Tweet classification model that relied on the textual content, user profile, and sentiment for positive or negative words. They exploited four supervised machine learning classifiers to generate a fake news classification model, including RF, DT, AdaBoost, and LR. The experimental evaluation showed that their model performance increased after applying sentiment features. The shortcomings of this work include considering solely the emotion of users who shared the news Tweets and the small size of the dataset. After reviewing the recent work on false news detection, we note the following gaps. The detection of Arabic Tweets containing fabricated news is still in its early stages. As a result, few Arabic datasets for detecting fake news Tweets are publicly available to the research community. There have been few studies conducted to address the Arabic Tweets' veracity using deep learning techniques, and the majority of them focus on detecting fake news relating to a certain topic, such as COVID-19. Additionally, they relied only on Tweets text to produce a classification. A major challenge for these identification systems is that the underlying textual characteristics vary under different fake news. For this reason, models that used only textual content of news Tweets may have a generalizability issue. In contrast, machine learning models commonly use news content and social context features to more accurately identify various types of fake news. However, the existing models' detection performance still needs to be improved.