# MaLang: A Decentralized Deep Learning Approach for Detecting Abusive Textual Content
## 2 Related Research Work
Due to their ability to identify spoken language, CNNs are frequently used to build models for detecting bullying tweets [8; 38; 39; 40; 41; 42]. Abusive language detection was performed by training Bidirectional Encoder Representations from Transformers (BERT) on a Reddit Abusive Language Dataset. Hate speech detection in different languages was achieved using various hybrid deep learning models such as Bidirectional Encoder Representations from Transformers (BERT) [41], Graph Neural Network Model [43], BERT-CNN [44; 45], Gated Recurrent Units (GRU) [46], CNN-GRU and mBERT, CNN-LSTM (Long Short-Term Memory) [10], and Heterogeneous Neural Interaction Networks [47]. Machine learning ensemble models for cyberbullying detection have also been proposed [12; 48], though more infrequently. The deep learning architectures used for experimentation include the specialization in regularization, convolution, and bi-directional networks in which the authors defined various metrics for evaluation. It is also observed that the LSTM models are provided with more parameters for training than GRU, which performs with the highest median accuracy with minimal metrics. The major issues with the hybrid models are that the experimental results and evaluations are bound to a single dataset [10; 28]. Automatic improper conversation (such as disgusting language and offensive chats) extraction is complicated and time consuming [32; 49; 50; 51]. Moreover, the processing of huge knowledge bases on resource-constrained platforms such as on-boards and mobile devices is very difficult. Increasing the number features increases the difficulties in the process of feature extraction and selection. No current research uses a word's meaning or semantics for cyberbullying detection [52; 53; 54]. Transformers, mentioned in a few papers stated above, often require high computing resources while training, especially at regular intervals. These models combined with different types of recurrent neural networks produce predictions with decent accuracy and precision. A novel approach is introduced to detect and restrict abusive textual content being exchanged in a network with better accuracy and precision. MaLang solves the challenge of detecting abusive text at both the edge and cloud levels, without consuming high computation resources and producing optimal predictions, while dealing with small influx of data at regular intervals. Table 1 provides a summary of the various state-of-the-art approaches and the accuracy of these models for detecting bullying language. Major Contributions of MaLang
* MaLang uses two-stage abuse detection and restriction analysis as opposed to more traditional single-stage detection processing. * At the edge level, MaLang uses Bi-LSTM-based CASE to detect toxic text, and at the Cloud level, KIPP utilizes a combination of Bi-LSTM- and Bi-GRU-based models for Entity-based recognition. This combination of machine learning approaches has not been used previously. MaLang proposes an architecture that keeps data private at the user level and the cloud level, without disclosing or transmitting the profile of the user involved in the system. Thus, data privacy and confidentiality are a central focus of the MaLang system design. * Unlike simple RNN approaches, MaLang uses an actively monitored pipeline to detect and alert users and administrators of possible toxic textual content being exchanged.