# Use of Data Augmentation Techniques in Detection of
## 5 Discussion and Conclusions  
The work presented in this paper focused on data augmentation techniques applied to text classification in the antisocial behavior detection domain. The main objective was to explore the possibility of using simple EDA augmentation techniques to overcome the class imbalance problem when solving antisocial behavior detection tasks using deep learning models. We evaluated EDA methods on two selected tasks--fake news detection and toxic comments classification. In both cases, we used the CNN classifier and compared its performance when trained on the original training set with training sets enhanced using a combination of EDA techniques. The effect of EDA augmentation techniques on the model performance is strongly dependent on the dataset. Although there are multiple EDA techniques available, those are usually very well used when applied to a dataset containing the texts written in more formal language. It was evident on the performance boost of the CNN model on the fake news dataset, which was significant, improving the F1 score by a 0.1.  
From the perspective of the style and language of the texts, EDA techniques applied to the fake news dataset positively affected classification performance. This dataset comprised news pieces usually longer than discussion comments and written using more formal language. This task was much better suited to the EDA synonym replacements and similar techniques. Using EDA, we could correctly generate the augmented data samples, contributing to model performance improvement. On the other hand, EDA techniques applied to the dataset of toxic comments did not improve the CNN model performance (only a 0.01 improvement in F1). The dataset mostly comprised short texts (discussion posts) and contained much nonformal content (e.g., slang expressions). Therefore, EDA methods relying on synonyms replacement were unable to find suitable synonyms for many of the words typical for toxic behavior in the comments. The application of these methods did not generate the augmented toxic samples suitable enough to be used to improve the model's performance.  
In general, the problem of enhancing the datasets (e.g., due to data scarcity or to balance the classes) in the NLP domain is very difficult and attracts the attention of many research groups. The ability to artificially generate new texts is a difficult task, and it is very challenging to synthetically generate the features present in the texts written by humans (such as irony or sarcasm). Simple augmentation techniques such as EDA cannot reflect these complex issues and even simpler ones, such as considering the context of replaced words. However, its simple implementation and application while maintaining reasonable performance can present an advantage in certain applications. In the future, we expect that exploration of the usage of a character-level augmentation method could be useful, as they can generate texts which can represent spelling mistakes (which are very common in this type of data). The further analysis and exploration of the suitability evaluation of other, more advanced augmentation methods such as GAN in this domain could be interesting.  
Conceptualization, M.S. and V.M.-K.; methodology, M.S.; software, V.M.-K. and J.J.; validation, V.M.-K.; formal analysis, M.S.; investigation, V.M.-K. and J.J.; resources, M.S.; writing--original draft preparation, V.M.-K. and M.S.; writing--review and editing, V.M.-K. and M.S.; supervision, M.S.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by VEGA grant number 1/0685/21.  
Not Applicable, the study does not report any data.  
The studies of the deep learning approach presented in this work are supported by Slovak VEGA research grant No. 1/0685/21.  
The authors declare no conflict of interest.