# Cross-Domain Fake News Detection Through Fusion of Evidence from Multiple Social Media Platforms
## 5 Experimental Setup  
In our study, we utilized the 'Celebrity' dataset, which comprises 480 samples. We partitioned the dataset into two distinct sets for training and validation to thoroughly validate our models. Specifically, 80% of the samples were allocated for training, while the remaining 20% were reserved for validation. Initially, we determined the optimal number of hidden layers and epochs using this configuration. To achieve a robust assessment of the threshold value (theory in Section 3), we conducted experiments leveraging this validation size to enhance the reliability of our evaluation. Additionally, we used the 'FakeNewsAMT' dataset, which also comprises 480 samples, exclusively for testing. Since we aimed to perform cross-domain analysis, we ensured that no data from the 'FakeNewsAMT' dataset were used for training or validation purposes. This approach allowed us to evaluate the model's performance and generalization capability on entirely unseen data. For our experiments, we used accuracy, precision, recall, F1-score, and false negative rate (FNR) as performance metrics, with fake news considered as the positive class. This comprehensive evaluation framework allowed us to thoroughly assess the effectiveness and robustness of our proposed EFCD model. We employed the Python 3.8 scikit-learn library to develop the models in our experiment. Scikit-learn is a robust machine learning library in Python 3.8 that provides simple and efficient tools for data mining and data [67]. For the first model, \(M_{1}\), we employed a linear regression (LR) approach. Linear regression is a fundamental statistical technique used to model the relationship between a dependent variable and one or more independent variables by fitting a linear equation to the observed data. In our implementation of the \(M_{1}\) model, we utilized the default parameters provided by the scikit-learn library. These default parameters include using the ordinary least squares (OLS) method to minimize the sum of the squared residuals between the observed and predicted values. This setup allows for straightforward interpretation and implementation, making it an ideal choice for the initial screening in our study. For the second model, \(M_{2}\), we used a deep neural network (DNN), implemented as a Multilayer Perceptron (MLP). This model was fed with the output of 768 features extracted from BERT to produce a binary output: fake or real. We evaluated the performance of the DNN model by computing the mean square error (MSE) and the regression scores (R2-Scores). Additionally, we utilized the 'Adam' optimizer for MLP, which is widely recognized for its efficacy in weight optimization. The ReLU (rectified linear unit) function was chosen as the activation function for the hidden layers. As depicted in Figure 3, the MSE diminishes as the number of hidden layers increases, reaching its lowest value of 0.1446 at the fifth hidden layer. Conversely, the highest R2-Score, 0.4217, is also achieved at the fifth hidden layer. We also validated our deep neural network (DNN) model using MSE as the loss value and as a performance indicator across different numbers of epochs with a batch size of 200. The loss values are plotted against epoch numbers in Figure 4. Figure 4 shows that the loss value gradually decreases up to 500 epochs and then increases again. The model obtained the highest precision for 500 epochs with a minimum loss value of 0.1446, which motivated us to use 500 epochs in our experiment. After selecting the hidden layer, neuron, and epoch, we aimed to set a threshold by which we decided whether to choose content or comment. To determine the best threshold value, we experimented with different thresholds using 0.2 validation size and observed accuracy, precision, and recall, shown in Table 5. We found that decreasing the threshold increases the recall value, which represents our fake news detection accuracy. Conversely, a higher threshold results in increased precision. The highest precision was observed at a threshold of 0.2.