# Use of Data Augmentation Techniques in Detection of
## 1 Introduction
In this experiment, they trained three models: LSTM, BERT, and the TF-IDF model. As in previous studies, the authors [19] focused on detecting toxicity in online comments. The authors divided this research into two parts. In the first part, they used a binary classification to detect toxic comments correctly. In the second part, they used a multiclass classification model to determine the degree of toxicity. Deep networks can address different types of toxicity in the text using multilabel classification [20; 21]. Capsule networks are also used to track the temporal aspects of toxicity in the comments [22]. The use of ML models to detect antisocial behavior from the texts is well studied, and models based on neural networks often prove to be the most suitable for handling these tasks. However, there are still many open research issues. One of the problems lies in the lack of well-labeled data. Even if many public datasets are available (in multiple antisocial behavior detection areas), many of them are still human-labeled, which may incorporate bias into the data. On the other hand, automatic labeling (e.g., using lexicons) may be more efficient in processing more data but still is not very reliable. To address the bias which can be introduced by human labeling, techniques such as crowd sourcing can be utilized. In addition, many datasets in this domain are heavily imbalanced. Such class imbalance may influence the detection models' performance, especially in the minor class, which usually represents the type of antisocial behavior (e.g., fake news articles, fake reviews, or toxic comments). Therefore, exploring the approaches that can sample the data in the minor classes to overcome the lack of data can be interesting. An important issue is generating new, artificial samples with the same characteristics as the original data. In work presented in this paper, we focused on using data augmentation techniques to improve the class imbalance by generating new, artificial samples from minor classes. We used simple text transformation methods based on vocabularies which were used to construct the new samples by replacing certain words with their synonyms. Such methods were already experimentally evaluated in several domains, e.g., clinical literature [23], sentiment analysis [24], or more recently in local (Portuguese) fake news detection [24]. The main motivation of our research was to focus on the antisocial detection behavior domain. We selected two typical tasks within this area which often involve processing imbalanced data. Then we experimentally evaluated if the application of EDA in these tasks could influence the performance of the detection models. We decided to evaluate both, separate EDA techniques as well as a combination of all EDA methods applied at once. We compared the performance of the classification models on the original and EDA-extended datasets. The paper is organized as follows: Section 2 describes the data augmentation methods used in the text processing domain. The following section presents the datasets used in the study and their preprocessing. Section 4 presents the deep learning model used in both evaluated tasks and presents the results of the experiments. Section 5 presents the conclusions of the experiment's results.