# Aggression Detection in Social Media from Textual Data Using Deep Learning Models
## 1 Introduction  
The internet has proven to be an excellent tool for public participation. Many meaningful conversations are conducted on social media via the internet. Moreover, people rely on social media to communicate their; social media has essentially transformed the whole world into a global village. However, this rapid shift toward digitalization comes with concerns, one of which is cyberbullying. Cyberbullying involves harassing, humiliating, threatening, or hassling other people using social media platforms. It is challenging to reduce cyberbullying due to anonymous profiles. Since cyberbullying leads humans to psychological and emotional disorders, online bullying is more dangerous than physical bullying. According to [1], 8% of social media users are unaware of cyberbullying, while many of them may be bullied. Cyberbullying can be classified into different types, such as racism, sexism, cyber aggression, etc. According to [2], cyber aggression involves aggressive or hateful behaviortowards others on online platforms. Hate based on race, nationality, religion, color, gender, and other factors, are considered the main motive of cyber aggression [3]. Online bullying may lead to similar issues in all people, irrespective of age. According to a new survey [4], almost 3.3M Facebook posts and 4.5M tweets are posted by people every single minute. However, these statistics are increasing each day. By analyzing and extracting reviews on particular issues, products, or celebrities, one can see that these vast amounts of tweets or Facebook posts often contain offensive text. According to [5], cyberbullying is a serious danger to social media users as the victims of cyberbullying become susceptible to many problems, such as low self-esteem, fear, anxiety, anger, and even suicide. According to statistical results obtained by [6], 25% of internet users are bullied, and 1 out of 3 teenagers are threatened online. Furthermore, about 145M active users daily post tweets on Twitter, regarding many topics, while 36% of these social media users were bullied in the year 2020 [7]. It is difficult to stop cyberbullying on social media; however, intelligent solutions could alleviate the problem of online bullying. Keeping in mind the requirements of automatic recognition of cyberbullying contents, machine learning and deep learning models are apparent. Cyber aggression detection is a fundamental problem of natural language processing (NLP). The critical part is to preprocess the data to remove unwanted text and tokenize the data for further processing. Afterward, feature extraction and selection are performed to prepare the data for input to machine learning or deep learning models. Much research has been conducted using conventional machine learning models. The study in [8] employed traditional machine learning models to perform text classification. They used support vector machine (SVM), logistic regression (LR), and naive Bayes (NB) as classification models. The authors of [9] also performed a comparative analysis of the machine learning models. Another study [10] used naive Bayes and SVM to identify cyberbullying in Arabic text. The limitation in [9] is that they only used textual features and basic machine learning models. However, it is difficult to achieve significant improvements in cyber aggression detection using machine learning algorithms while using traditional feature extraction methods, such as term frequency-inverse document frequency (TF-IDF), Word2 Vec, etc. Identifying emotions without facial expressions and voice tones is still an operose task. Understanding emotions from textual data is difficult as there is limited text length on Twitter and the use of internet slang and sarcasm. To overcome this difficulty, deep learning involves robust models, such as the dense neural network (DNN) [10], multilayer perceptron (MLP), long short-term memory (LSTM), recurrent neural network (RNN), and convolutional neural network (CNN).