# MaLang: A Decentralized Deep Learning Approach for Detecting Abusive Textual Content
## 6 Conclusions  
Electronic communications are becoming ever more ubiquitous in today's organizations as well as in general life. The ever-increasing use of electronic communication technologies has enabled and is increasing the occurrence of workplace cyberbullying and the exchange of other hostile or abusive communications [3]. Various attempts were previously made to utilize machine learning to try and identify abusive language in textual communications but are not being widely deployed due to system overhead or other business factors. In this paper, a new method for identifying and reacting to abusive text messages, called MaLang, is developed and tested.  
The MaLang approach ensures data privacy by not sharing sensitive or raw data being transmitted to the cloud. With the increasing global emphasis on data privacy, MaLang's anonymization features could be critical adoption factors for organizations.  
The Deep Learning models CASE and KITP perform with an average accuracy of 92.2% and 98.21%, respectively. They achieved an average AUROC score of 0.977 and 0.986, respectively, in classifying abusive content. With the methodology presented in the current paper, it can be concluded that MaLang outperforms existing methods with respect to overall accuracy for detecting abusive textual content in a user's electronic communications using their device and over the business's network.  
The MaLang framework can be deployed in an organization to detect the exchange of abusive content among employees, even across different departments. The methodology to improve the model by updating the weight matrix forms the basis of federated learning and helps multiple devices benefit from the mechanism of training one model on multiple datasets. The decentralized approach can be used to detect abusive textual content in the downloaded media on user's device.  
A potential future upgrade to the framework would scale down the functionalities of Cloud-level classification to an edge platform preserving data securely and performing classification on mobile devices without pushing any data to the cloud. The approach can be scaled up to perform real-time text prediction without suggesting any abusive content to the user to type in. This could potentially decrease the usage of abusive or toxic content.  
Conceptualization, S.C.S. and P.K.; Methodology, S.C.S. and P.K.; Software, S.C.S., P.K. and M.V.C.; Validation, S.C.S. and S.W.; Formal Analysis, S.J.; Data Curation, S.J.; Writing--Original Draft Preparation, S.C.S.; Writing--Review & Editing, S.W.; Visualization, S.C.S. and S.W.; Supervision, S.W. All authors have read and agreed to the published version of the manuscript.  
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.  
Not applicable.  
Publicly available datasets were analyzed in this study. This data can be found here: [[https://www.kaggle.com/mrmori/hate-speech-and-offensive-language-dataset](https://www.kaggle.com/mrmori/hate-speech-and-offensive-language-dataset)]([https://www.kaggle.com/mrmori/hate-speech-and-offensive-language-dataset](https://www.kaggle.com/mrmori/hate-speech-and-offensive-language-dataset)), accessed on 30 August 2021.  
The authors declare no conflict of interest.