# MaLang: A Decentralized Deep Learning Approach for Detecting Abusive Textual Content
## 3 Materials and Methods  
To overcome issues encountered in the previous related work described above, this paper proposes a novel approach called Malang. Malang is a decentralized learning approach to detect abusive textual content and initiate managerial response to detected cyberbullying events. MaLang leverages the use of Bi-LSTM with a Max Pooling Layer for abusive content classification in system-level modules and the use of Bi-LSTM and Bi-GRU with a combination of Average and Max Pooling layers. The cloud-level modules are interconnected for different departments in the workplace when deployed in medium to very large organizations. When a tagged entity is received in one module, a copy is sent to other cloud modules in the network to check if a similar entity is present. In the case of a match, it is concluded that two or more people were involved in the sharing, and an alert is sent to the Human Resource representative and the cyber protocol enforcement department.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **Reference** & **Methods and Models** & **Accuracy** \\ \hline Text Analytics & [22] & Keyword matching & 58.6\% \\ \hline \multirow{2}{*}{Natural Language Processing} & [7] & \multirow{2}{*}{Sentiment Analysis} & 95\% \\ \cline{2-3}  & [53] & & N/A \\ \hline \multirow{4}{*}{Machine Learning (ML)} & [23] & \multirow{2}{*}{C4.5 decision tree} & 78.5\% \\ \cline{2-3}  & [20] & & 77\% \\ \cline{2-3}  & [21] & & 77\% \\ \cline{2-3}  & [30] & & 78\% \\ \cline{2-3}  & [48] & & 90.57\% \\ \cline{2-3}  & [12] & & 91\% \\ \hline \multirow{4}{*}{Machine Learning (ML)} & [35] & \multirow{4}{*}{CNN} & 81\% \\ \cline{2-3}  & [33] & & 84\% \\ \cline{2-3}  & [34] & & 92.8\% \\ \cline{2-3}  & [50] & & 96\% \\ \cline{2-3}  & [53] & & 73.2\% \\ \cline{2-3}  & [40] & Deep Neural Network & 86.6\% \\ \cline{2-3}  & [8] & & 93.97\% \\ \cline{2-3}  & [39] & & 92.7\% \\ \cline{2-3}  & [38] & LSTM & 93.97\% \\ \cline{2-3}  & [55] & Bi-directional LSTM & 52.6\% \\ \cline{2-3}  & [42] & Tree and LSTM & 90.3\% \\ \cline{2-3}  & [45] & Gradient Boosting BERT and LASER & 62\% \\ \cline{2-3}  & [56] & CNN-GRU and mBERT & 95\% \\ \cline{2-3}  & [10] & CNN-LSTM and CNN-BiLSTM & 92\% \\ \hline \multirow{2}{*}{[46]} & [46] & LSTM and GRU & 98\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: State-of-the-art research on cyber bullying detection.  
As shown in Figure 1, the automated business-level cyberbully detection framework is divided into two modules: System Level and Cloud Level. The System-level module consists of the deep learning model called 'CASE' that classifies abusive content from non-abusive content on the user's device. The collected abusive content is transmitted to the cloud to detect the intensity of the abusive content. The Cloud-level module consists of a cloud-based deep learning model called 'KIPP' that classifies the level of Toxic Content of each sentence into one of four classes: 'Toxic', 'Threat', 'Insult', or 'Hate Speech'. A sentence can contain multiple classes of Toxic content as presented in Table 2, which shows examples of partial real-world organizational messages and their corresponding classifications (the second message has been modified for viewing purposes).