# Text Data Augmentation Techniques for Fake News Detection in the Romanian Language
## 3 Related Work
The detection of fake news for Hindi obtained an F1 score of 0.68 in the case of the Random Forest Classifier and the Support Vector Machine, and better results were obtained for Logistic Regression, with an F1 score of 0.875. For our experiment, the results are lower in the case of the context-based data set, with an F1 score of 0.78 for the Support Vector Machine and Logistic Regression, and a small increase to 0.81 for the Random Forest Classifier. The slight difference of 6% in the results can be explained by language structure and characteristics or by differences in the number of words per record (but we are not aware of this aspect for the Hindi data set). Another interesting study presents a Persian stance detection approach that includes data augmentation as a method to improve the training data set [14]. The authors applied EDA to solve the problem of small data sets in Persian stance detection tasks. However, the fake news detection in the Persian study is different from our approach because it applied DL methods. It is worth mentioning that the authors investigated the effect of data augmentation and the ParsBERT pre-training model and concluded that EDA obtained better results than in previous works. A similar situation was observed in the case of fake news detection based on posture using pre-trained BERT [15]. For that study, the authors experimented with random swap augmentation and synonym replacement techniques separately and demonstrated that swap augmentation provided the best result. In the paper [16], [PERSON] et al. investigated the use of text data augmentation for the stance task and fake news detection. The authors concluded that the text augmentation approach achieves promising predictive performances. Data augmentation was applied to detect antisocial behavior along with DL methods [17]. However, the DL approach is different from our approach; it is interesting to observe that EDA is used to overcome problems such as class imbalance. The authors stated that EDA is very task-dependent and has limitations; the method was not suitable enough to improve the results in the case of toxic comment classification, but significantly improved the performance in the prediction of the minor class for the detection of fake news [17]. The application of a specific process from the EDA method has also proven to be efficient. For example, the study [18] presents a synonyms-based augmentation approach to improve the detection of fake news using Bidirectional LSTM. Even if the architecture is different from the one we present in the current study, it is important to mention that applying the synonym augmentation process increased the prediction capability of the system compared to the experiment that does not include data augmentation. Furthermore, the authors aim to develop better text augmentation processes to improve model prediction [18]. There are novel approaches to text augmentation that aim to solve the same problems. These approaches include DL or transformer-based methods, and, in general, they manage to outperform some of the current methods [19; 20; 21; 22; 23]. Future investigations related to transformer-based augmentation methods can be conducted to develop high-performance models for fake news detection. For the Romanian language, we have identified similar approaches that aim to automatically detect fake news. For example, [PERSON] et al. [24] propose a supervised ML system to detect fake news in online sources published in Romanian. They compare the results obtained for recurrent neural networks and BERT to the results achieved by two basic classification algorithms, namely, Naive Bayes and Support Vector Machine. Thus, the results of the Support Vector Machine approach will be compared with the performances obtained in our experiment. The Romanian data set constructed by [PERSON] et al. [24] consists of 25,841 true records and 13,064 false records. The data set was collected from multiple sources between 2016 and 2021 and was validated by 12 employees in a public institution in Romania.