## 4 Main Algorithm
Our performance evaluation has shown that, in comparison to ML models, the proposed TLA-NET model achieved the best accuracy performance on the raw Bangla dataset. Overall, the results obtained from traditional models are not satisfactory. However, our developed model has overcome the shortcomings of existing models and has achieved excellent accuracy performance, as described. Using our approach, future researchers will be able to analyze various issues associated with text datasets that were previously neglected due to data availability. With our TLA-NET model, they will be able to detect cyberbullying with high precision on social media. Following the results of our study, we can derive several implications for future research efforts, which could signficantly extend our efforts. \[\textbf{MSE}=\frac{\sum_{i=1}^{n}(\textbf{predicted}(i)-\textbf{actual}(i))^ {2}}{n}, \tag{3}\]4 ROC curves for multi-class classification labels across different models. **Anomaly detection. ** When dealing with cyberbullying comments, a relevant problem relates to how to detect anomalies in text, perhaps following some state-of-the-art proposals (e.g., [47]). This will increase the quality and the accuracy of both the target dataset and the ML analysis performed on top of it. **Visualization metaphors. ** A relevant future research direction for cyberbullying detection and analysis is represented by the issue of supporting advanced visualization metaphors (e.g., [48]) in order to improve data visualization paradigms over cyberbullying activities (e.g., [49]). The latter property will improve the precision of the analysis and the effectiveness of actions for combatting cyberbullying in society. **Performance and scalability. ** Last but not least, the performance and scalability aspects of algorithms for supporting cyberbullying detection and analysis play a relevant role, like in actual big data trends (e.g., [50; 51; 52]). In fact, social media can easily originate datasets with classical 3V characteristics (volume, velocity, and variety), so specialized solutions are necessary to ensure executions that scale with the data growth. Conceptualization, A.C. and M.S.A. ; Methodology, A.C. and M.S.A. ; Validation, H.S. and P.G.B. ; Data curation, M.S.A. ; Writing--original draft, M.S.A. ; Writing--review & editing, A.C.; Supervision, H.S. All authors have read and agreed to the published version of the manuscript. This work was partially supported by project SERICS (PE00000014) under the MUR National Recovery and Resilience Plan funded by the European Union-NextGenerationEU. The datasets generated during and/or analyzed during the current study are publicly available. The authors declare that they have no conflicts of interest. They also declare that they do not have any competing financial interests. ## References

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