# Aggression Detection in Social Media from Textual Data Using Deep Learning Models
## 2 Related Work  
Rapid advancements in the'social web' have paved the path for conducting research in the field of NLP. Considering the social web as an active area of research, researchers have used different approaches, such as machine learning, deep learning, and NLP to identify the sentiment polarity expressed in various social media platforms. Social media applications, such as Twitter, weblogs, Facebook, etc., have become diverse language platforms where people of different languages, races, and communities comment every day on various topics, images, celebrities, etc. Consequently, the diversity of users on social media could lead to bullying, aggression, and hate speech threads. Due to the complexity of foreign languages, handling this kind of online behavior is a challenging task. Some related terminologies used by researchers to represent aggression include cyberbullying [12; 13; 14], offensive language [15; 16], hate speech [17; 18; 19], racism [20], and profanity [21]. The author in [22] compared cyber aggressors to non-cyber-aggressors and analyzed whether a non-cyber-aggressor consults a psychologist more than a cyber aggressor. The authors in [23] used text, network, and user-based features to detect aggression in a Twitter dataset. Moreover, they suggested that bullying victims relatively write fewer posts and participate less in discussions, while aggressors often participate in online discussions and try to put out more negativity. Another study [24] used the Lexicon-based method to identify hate speech. They used sentiment lexicon to perform a subjectivity analysis and achieved a 70% F1 score. The authors of [25] showed that the performance of offensive language detection could be improved by choosing effective text features, such as verbs, nouns, adjectives, several emoticons, and others. The majority of research into computational linguistics often focuses on languages rich in resources, such as English. However, researchers have paid less attention to resource-poor languages due to the lack of resources, such as prepared datasets. However, numerous regional researchers have used machine learning algorithms to detect offensive language in social media posts of various languages. For instance, the authors of [26] presented their achievements in aggression detection in Hindi and Marathi, local Indian languages. They used bag-of-words (BOW) with multinomial-naive Bayes (MNB), stochastic gradient descent (SGD), and LR as classification techniques. They used a small dataset and did not apply any deep learning models. Similarly, ref. [8] performed their experiments in Arabic text on Twitter by considering the strength of bullying messages. Article [5] focused on the analysis of user credibility to classify Indonesian text messages as bullying or non-bullying, by using state-of-the-art approaches of SVM and KNN. Moreover, ref. [27] performed their experiments on Twitter data for aggression detection using a single feature, i.e., TF-IDF, and a straightforward method, MLP. The technique presented in [27] can be improved further by the features proposed in this paper. A study conducted by [28] made a machine learning model for offensive language detection in German text. Another study [29] applied various models of machine learning to classify tweets in German to detect offensive text. A classifier ensemble was used in the study [30] to detect offensive text from web pages in Portuguese. Moreover, an n-gram was used by [17; 31] to detect offensive language in Arabic text from online comments. The study by [32] used a support vector machine, naive Bayes, and a decision tree with n-gram features of grouped words to detect abusive text on Indonesian social media. Similarly, other researchers [33; 34] also performed experiments to detect abusive text using the Indonesian language. The majority of researchers performed extensive experiments to identify cyberbullying detection on English datasets. Several studies are discussed below.