###### Abstract
In our experimental study, we considered the reference Bengali Cyberbullying Text Dataset [31], which provides a reliable testbed for performance evaluation and assessment. Also, our comparison is based on standard evaluation metrics, including accuracy, precision, recall, and F1-score [32], and advanced evaluation metrics [32], including mean absolute error (MAE), mean squared error (MSE), and root mean square error (RMSE), plus other components, like confusion matrices [32] and receiver operating characteristic (ROC) curves [32]. We also consider, as competitive algorithms, some well-positioned-in-the-community machine learning (ML) algorithms, like the decision tree (DT) [33], random forest (RF) [34], logistic regression (LR) [35], and multilayer perceptron (MLP) models [36]. The proposed TLA-NET model delivers state-of-the-art results with an accuracy of about 99% in the raw Bangla dataset. It also achieves top-notch performance despite the presence of partially noisy datasets. Therefore, our method can be valuable for languages with limited data availability. Following our approach, efforts to reduce suicide-related incidents can be somewhat aided through the detection of aggressive comments and the implementation of prediction-based measures (e.g., [37]). On the other hand, collecting social media data raises the very relevant problem of ensuring the privacy of users (e.g., [38; 39]). While this problem is of evident significance, it is not addressed in our research, and is left for future work to consider. All considered, the primary contributions of this article can be summarized in three aspects, as follows:  
1. TLA-NET: a trustable LSTM-autoencoder network for cyberbullying detection and prevention on social media using synthetic data. The model follows the basic LSTM-autoencoder framework and is capable of delivering highly accurate results. We achieve state-of-the-art results using our proposed model. 2. We outline a process to generate synthetic data, thus achieving the so-called noisy synthetic dataset. 3. We conduct extensive experiments by comparing TLA-NET against state-of-the-art ML algorithms on top of a reference dataset on Bangli cyberbullying text and adopting several authoritative evaluation metrics. This step proves essential for comparing various neural network models under varying levels of noise intensity in the dataset. This paper is an extension of our previous paper [40], where we provided the initial ideas about the framework and a preliminary classification algorithm as a first prototype. The remaining part of this paper is organized as follows. Section 2 focuses on reviewing several related works in the context of cyberbullying detection, prevention, and analysis on social media. In Section 3, we examine and describe the methodological approach that we adopted to investigate the phenomenon of cyberbullying within social networks, by presenting our proposed TLA-NET model and the datasets used in our experimental campaign. Section 4 presents the main algorithm implemented by our technique. After that, Section 5 provides a detailed overview of the results obtained through the rigorous application of our proposed TLA-NET model. Finally, Section 6 provides the conclusions of our research and sketches possible future directions.