# The Detection of Fake News in Arabic Tweets Using Deep Learning
## 4 Experiments and Results
### Experimental Results  
We performed the classification experiments with different parameters and selected the final configuration that achieved the best possible performance on the testing set. The optimal configuration for both the CNN- and BiLSTM-based models are described in detail below and summarized in Tables 6 and 7. The architecture of the CNN-based model comprises three parallel convolution layers, three max pooling layers, followed by two fully connected layers. A fixed value to the number of filters is used for the CNN layers, which is 64 with kernels of sizes 1, 2 and 3. The two dense layers contain 512 and 256 neurons, respectively, and ReLu is used as the activation function. A dropout of 0.5 is applied to the output of each dense layer. The optimization technique Adamax was used with a learning rate of 0.004, and categorical_crossentropy was used as the loss function. The design of the BiLSTM-based model \begin{table}
\begin{tabular}{c c c} \hline \hline
**Processing Layer** & **Parameter** & **Experimental Range** \\ \hline \multirow{2}{*}{CNN} & Kernel size & [1, 2, 3, 4, 5, 6, 7] \\  & No of filters & [16, 32, 64, 128] \\ \hline BiLSTM & No of LSTM units & [16, 32, 64, 128] \\ \hline \multirow{4}{*}{Dense} & No of neurons in the dense layer & [128, 256, 512, 1024] \\  & Dropout rate & [0.1–0.5] \\ \cline{1-1}  & Optimizer & [’Adamax’, ’Adam’, ’SGD’] \\ \cline{1-1}  & Learning rate & [ 0.001, 0.002, 0.003, 0.004] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Hyperparameters of the proposed model. consists of a BiLSTM layer, a global max pooling layer, followed by two fully connected layers. The first layer is a BiLSTM layer, forward and backward LSTMs, with 128 neural units. The output features are passed through the two dense layers of the 512 and 256 neurons, respectively, with the ReLu activation function. Dropout layers with a probability of 0.3 are used after each dense layer. The Adamax optimizer with a learning rate of 0.002 is used as the optimization function while the loss function was set to categorical_crossentropy. After tuning the models' hyperparameters, the two models were trained with five different feature extraction methods. In this work, four famous word embedding models (word2 vec, fastText, ARBERT, and MARBERT) and the Keras embedding layer were used. We relied on using published pre-trained word embedding models instead of training models to generate word embeddings. Training the models from scratch would require a large volume of data and is often time consuming. Tables 8 and 9 show the evaluation results using CNN- and BiLSTM-based models. Both models were evaluated by applying a five-fold cross-validation, and the final results are reported by averaging the performance metrics yielded from all the testing sets. The performance metrics include accuracy, precision, recall, and F1-score. with only the textual features of the news Tweets. Figure 4 presents the obtained results for the combination of textual and user features, and with only textual features. When only textual features were used, the evaluation result for the CNN model was decreased by approximately 3.0%. This result proves that incorporating user features with textual features improves the prediction performance of the proposed model.