# Text Data Augmentation Techniques for Fake News Detection in the Romanian Language
## 1 Introduction  
The spreading rate of false information across digital and traditional news platforms has become a serious problem over the past few years, with the principal contributing factor being the distorted nature of the data. Individuals face challenges in distinguishing between reliable and denatured media sources. The ability of individuals to differentiate between real news and false news is directly affected by the amount of genuine information available on different media platforms. The way the human mind works is influenced by the existence of reliable references across all domains. The high volume of fake news determines major changes in the level of trust people have in media sources. Fake news refers to the manufactured information created to distort people's understanding of events. The spread of fake information has made specialists from different domains determined to implement fake news detection methods to identify and mark information that aims to affect people's understanding of reality. In this context, Natural Language Processing (NLP) has become an important technique for the fake news detection process. NLP is a subfield of Artificial Intelligence that allows computers to understand and process human languages. Over recent years, NLP has proved its efficiency for multiple tasks such as text summarization, question answering, machine translation, text classification, etc. [1]. Unfortunately, there is one big challenge that frequently occurs in the context of certain NLP tasks involving not-so-common languages--the lack of large and reliable data sets. To overcome this problem, augmentation techniques for text data have gained interest. Data augmentation is a well-known method for improving Machine Learning (ML) models by creating data outof data; the scope is to increase the quantity and diversity of training data sets [2]. Text data augmentation is the process of creating new text data that are similar to the original text information. Increasing the size of data sets is important for NLP tasks because it allows ML models to increase their robustness. One of the common problems in the field of NLP tasks when considering languages that are less commonly used is the lack of reliable data sets. This is a well-known challenge that has a negative impact on training the supervised ML algorithms and implicitly affects the process of making accurate predictions when it comes to NLP tasks. Another challenge in this area is related to the size, origin, and reliability of the data. Another point involves the difficulties that researchers encounter when trying to identify sources of annotated data that align with their specific problems and can be used further for data set creation. Annotated data are challenging to identify and represent the key to obtaining good results from supervised learning techniques. The most common approach to data set creation involves careful analysis of online data sources to address certain problems, rigorous data collection processes, and complex preprocessing steps to ensure that the data are suitable for applying computational algorithms. Unfortunately, these steps do not fully overcome this common challenge. Usually, more techniques are required to obtain larger data sets that can be used to train ML algorithms. The size of the data sets can be increased by using text data augmentation methods, such as translation, back translation, synonym word replacement, generative models, random insertion, swapping, or deletion. These methods have been successfully proven to enrich training data sets, and, furthermore, they can solve other common problems, such as data imbalance, by generating additional samples for the minority output class. This article focusses on text data augmentation techniques for the Romanian language to improve the efficiency of fake news detection tasks. The original data set is extracted from a fact-checking site that evaluates and labels the political declarations of Romanian politicians.