# The Detection of Fake News in Arabic Tweets Using Deep Learning
## 2 Related Work
The findings revealed that the Logistic Regression (LR) classifier scored a high performance with an F1-score of 0.933 in both the manually and automatically annotated datasets. Their research failed to enhance the classification result using stemming and rooting techniques. A significant drawback is that the list of fake keywords regarding COVID-19 is limited. Thus, their model is not appropriate for use on other datasets. [PERSON] et al. [17] proposed a comprehensive approach that comprised two models. The first model was used to assess the reliability of Twitter posts relating to COVID-19, whereas tracking and identifying the source user who released the misleading news is the purpose of another model. They performed extensive experiments to detect fake news, including standalone and ensemble-based machine learning algorithms. The fake news detection approach comprises a stacking-based ensemble model that was applied to the Tweet's text and the Genetic Algorithm-based SVM (GA-SVM) model that worked on the content- and user-based features. The feature maps of both models were combined to construct a new training set, which was then fed into the GA-SVM classifier to predict the Tweet as a rumour or a non-rumour. The best F1-score achieved by the detection model was 0.935. [PERSON] et al. [18] presented a comparative study investigating the performance of state-of-the-art machine learning and deep learning models for rumour detection. They relied on the ArCOV19-Rumors dataset to train and evaluate their models. In the first experiment, they investigated the effect of n-gram, TF-IDF, and word2 vec on traditional machine learning classifiers. Then, they used two ensemble learning techniques, voting and stacking techniques, that combined the best classifiers to enhance the overall performance. The results showed that stacking classical classifiers performed better than single models, with an accuracy of 0.81. In the second experiment, they explored the influence of utilizing different optimizers with deep learning models. The LSTM and BiLSTM with the RSMprop optimizer earned the highest accuracy among the other neural networks with a score of 0.80. Combining information from both news content and social context sources may result in a better detection rate. Incorporating social context features, such as user behaviour, user profile, etc., with other news content features is uncommon in deep learning-based studies. Several efforts have been made to propose models for detecting misinformation using traditional machine learning algorithms utilizing both news content and social context aspects [1; 19; 20; 21; 22]. [PERSON] et al. [1] developed a machine learning-based model to detect Arabic fake news on Twitter. They examined eight well-known machine learning methods with multiple feature combinations. These features can be related to the news content itself or to the user who posted the news Tweet. The highest F1-score reached was 0.839, with the LR algorithm utilizing user profile and content-based features along with the TF-IDF representations of the textual data. The experimental results showed that the Harris Hawks Optimization (HHO) successfully improved the model's performance by approximately 3.0%. HHO was used as a feature selection approach to filter out irrelevant and redundant features. [PERSON] and [PERSON] [19] developed a model to assess the authenticity of news propagated by Twitter users. The proposed model comprised four modules. The first module is responsible for extracting content and social context features. The second module verified news against popular and reliable news sites, such as al-Arabiya ([[https://www.alarabiya.net](https://www.alarabiya.net)]([https://www.alarabiya.net](https://www.alarabiya.net))). The third module was employed to evaluate the polarity of users' replies. Three traditional machine learning algorithms, Decision Tree (DT), NB, and SVM, have been trained on extracted features. The DT classifier obtained the best accuracy at 0.899.