# MaLang: A Decentralized Deep Learning Approach for Detecting Abusive Textual Content
## 2 Related Research Work  
Around the world, more than one billion people use messaging platforms, such as WhatsApp or Facebook Messenger, and more than 30 billion messages are exchanged daily [19]. However, not all of these messages are beneficial to individuals or organizations as a lot of toxic textual content is shared in public, private, and workplace spaces. Abusive textual content includes words or phrases that either insult or show hate speech toward a person or community, or that pose a threat to a person, or project obscene opinions. Various applications have been deployed to control and minimize the sharing of such content [20]. Earlier information retrieval (IR) and natural language processing (NLP) methods are applied for resolving the text-related classification problems such as sentiment analysis [21]. IR and NLP have been previously applied for the detection of cyberbullying text-oriented messages [11]. Various other cyberbullying detection techniques have been previously developed, with most being dependent on textual and user features [7]. A simple program was developed by making use of a dictionary of keywords to perform cyberbullying detection over online chats [22], and performance was improved by collecting a much larger sample of labeled data [23]. Sensitive topics in the offensive and cyberbullying-oriented comments of YouTube such as racism or sexual aggression are considered in building the detection model [24]. The roles of bullies and other participants are studied and segregated into the groups of accuser, bully, and victim [21]. Feature engineering is a commonly used method for detecting cyberbullying from text-related representations. A twitter-based model extracts features from tweets and classifies them into the different groups mentioned in [21]. In addition, to generate additional features, the domain knowledge of data is also used for improving the performance of the classifiers. The performance of Support Vector Machines (SVM) is improved for the detection of cyberbullying as a result of adding features such as the occurrence and frequency of bad language, number of pronouns, length of comments, use of capital letters, and emoticons [20]. Similarly, pronoun counts are also considered a feature with the representation of n-grams and skip-grams [25]. The usage and application of soft computing for detecting cyberbullying are reviewed by [26]. A combination of textual and social network features is utilized for cyberbullying detection in [27]. Social network analysis (SNA) features are represented as a graph with the combination of nodes and edges used for discriminating the activities of cyberbullying. The usage of textual features along with the term frequency-inverse document frequency method improved the detection of cyberbullying. SNA has been further extended for the labeled features such as racism, sexual, and intelligence [28; 29]. The classification of more indirect and hidden labels related to bullying can improve the accuracy of cyberbullying detection. [PERSON] et al. [30] presented an insult-related word embedding method for extracting the features of bullies. They also used the additional features that were extracted from a dictionary of general words used in social networks [31]. However, their method works well only on a balanced dataset and suffers in real time with imbalanced datasets [32]. The issue of real time deployment and huge data processing is addressed by various researchers who used neural network-based models [33]. A simple convolutional neural network (CNN) model is utilized over pre-trained word vectors to perform classification based on the number of tokens presents in the target text [34; 35]. The CNN model was used on 50 million tweets for detecting cyberbullying [35; 36]. A pronunciation-based CNN is proposed to tackle the challenges of cyberbullying such as misspellings in the messages and posts in social media [37].