# Use of Data Augmentation Techniques in Detection of
## 1 Introduction  
In recent years, deep learning methods have achieved great success in solving various problems, including bioinformatics [1], cybersecurity [2], manufacturing [3, 4], or natural language processing (NLP). NLP deals with creating computational algorithms for the automatic analysis and representation of human language. In the field of NLP, neural networks achieve excellent results compared to traditional machine learning models, such as SVM (support vector machine) or logistic regression. In comparison to traditional machine learning algorithms, deep learning algorithms can learn multiple levels of representation. From the perspective of NLP, deep learning models (especially recursive neural networks) can also capture sequence information within the text (e.g., phrases), which makes them a more suitable option for NLP than the traditional methods. In recent years, convolutional neural networks (CNNs) have shown breakthroughs in some NLP tasks, such as text classification [5, 6, 7]. Nowadays, online platforms are a widespread phenomenon that enables users to communicate with different messages. Moving human communication to online platforms is adouble-edged sword. Benefits include the opportunity to share opinions and experiences and get immediate feedback, as well as the opportunity to discuss various topics. On the other hand, on these online platforms, we can observe vulgarities, hate speech, insults, or misinformation, which are referred to as _antisocial behavior on the Internet_[8]. Spreading misinformation on the Internet can take various forms, such as hoaxes, spam, rumors, false reviews, etc. We focused on two types of them, toxicity in comments and fake news [9]. Toxicity in comments is defined as a rude, disrespectful, or inappropriate comment that is likely to force other users to leave the discussion. Toxicity in comments can appear in various areas, such as social networks or discussions related to news articles [10]. The most common type of antisocial behavior on the Internet is fake news. They are considered news pieces that are intentionally and demonstrably untrue. Usually, these articles are designed to mislead, deceive, and influence people's opinions. Fake news contains false information, the veracity of which can be verified [11]. Manually detecting and tracking online content is a very demanding and costly process. Machine-learning systems that prescreen content and identify suspicious cases have proven successful in detecting antisocial behavior. These algorithms may prove to be a viable solution to problems on social networking platforms. Besides the traditional machine learning models, deep learning is very capable in the detection of various forms of antisocial behavior on the web. Deep networks (including different topologies of CNN) have been successfully used to automatically detect cyber-bullying in Twitter posts [12; 13]. Deep networks are successful in other related tasks, such as hate speech detection [14]. Besides the commonly used deep learning architectures, ensembles of deep networks can be used to improve the detection ratio [15]. Deep learning methods are very popular in toxic comment classification and fake news detection. The authors of the study [16] focused on the detection of fake news using neural network methods on two datasets that contained English news articles. To solve this problem, the authors used CNNs, RNNs (recurrent neural networks), unidirectional LSTM (long short-term memory), and bidirectional LSTM networks. In [17], the authors focused on binary toxicity classification in online comments. The authors used the k-nearest neighbors, naive Bayes, and CNN models to detect toxicity in the comments. The CNN network proved to be the most successful one. In contrast to the previous study, the authors of [18] focused on classifying toxicity in comments and minimizing identity bias. In this experiment, the authors showed that although the model works well on a dataset, it can still demonstrate bias at subgroup levels.