## 4 Main Algorithm
## 5 Experimental Analysis and Results

This section provides a detailed overview of the results obtained through the rigorous application of our methodology. Through graphs, statistics, and in-depth interpretations, we unveil the nuances and trends that outline the complex landscape of this digital phenomenon. The results presented here not only address the research questions but also open new perspectives and questions that will enrich the academic and practical discourse on the dynamics of cyberbullying within social networks. This study presents a hybrid machine learning approach for online cyberbullying detection. The developed procedure consists of several processes: (i) text preprocessing to prepare the comments, (ii) feature extraction to transform the processed text into numerical information, and (iii) resampling to balance the data. ### Experimental Setup

To create the proposed model, experiments were conducted by considering the Bengali Cyberbullying Text Dataset [31]. The usage of a specific dataset for the experimental phase improves our analysis. As regards the computational infrastructure, we made use of a workstation running the Microsoft Windows 10 Pro operating system with an Intel(R) Core(TM) i7-10700 CPU at 2.90 GHz, a 500 GB SSD, and 16 GB RAM. The programming language used is Python, along with standard libraries including Pandas, NumPy, Matplotlib, Seaborn, TensorFlow, Keras, Scikit-learn, and others. It should be further noted here that, in our experimental assessment and analysis, we only explore the Bangla language as the input, so as to better test the sensitivity of the results and the independence of the core technique, similarly to other studies in the field (e.g., [46]). As a comparison, we considered several state-of-the-art ML algorithms (see Section 1), namely DT, RF, LR, and MLP. ### Performance Evaluation Metrics

The parameters used to evaluate the model's performance are (see Section 1) as follows: accuracy, precision, recall, F1-score, MAE, MSE, RMSE, confusion matrices, and ROC curves. While accuracy, precision, recall, and F1-score are classical ML metrics [32], the other MAE, MSE, and RMSE parameters are defined as follows:

\[\textbf{MAE}=\frac{\sum_{i=1}^{n}\textbf{predicted}(i)-\textbf{actual}(i)}{n}, \tag{2}\]

\[\textbf{MSE}=\frac{\sum_{i=1}^{n}(\textbf{predicted}(i)-\textbf{actual}(i))^ {2}}{n}, \tag{3}\]

\[\textbf{RMSE}=\sqrt{\frac{\sum_{i=1}^{n}(\textbf{predicted}(i)-\textbf{actual }(i))^{2}}{n}} \tag{4}\]

where \(n\) is the total number of samples; \(\textbf{actual}(n)\) is the set of actual values with \(n\) samples; _predicted_(\(n\)) is the set of predicted values with \(n\) samples. Confusion matrices are defined as a tool used to evaluate the performance of a classification model on a test dataset. They are particularly useful when working with supervised classification problems, when one is trying to predict categories or classes for observations based on certain characteristics. ROC curves are a graph used to evaluate the performance of a binary classifier, such as an ML model that must distinguish between two classes, for example, positive and negative. ROC curves display the relationship between the true positive rate (TPR) and the false positive rate (FPR) as the classifier's decision threshold varies. The TPR represents the fraction of positive examples correctly classified as positive, while the FPR represents the fraction of negative examples incorrectly classified as positive. The ROC curve illustratesthe trade-off between the sensitivity (TPR) and specificity (\(1-\texttt{FPR}\)) of the classifier as the threshold varies. An ideal classifier will have an area under the curve (AUC) equal to 1, meaning that it can perfectly distinguish between classes. A random classifier will have an AUC of 0.5, as its discriminatory ability is akin to tossing a coin. The higher the AUC, the better the classifier's performance in distinguishing between classes.