# Enhancing Fake News Detection in Romanian Using Transformer-Based Back Translation Augmentation
## 1 Introduction  
Misinformation is spreading through modern media and social networks at an alarming rate. Fake news is a term used to describe news content that contains false, inaccurate, or deceptive information, and often uses more straightforward and repetitive content, which resembles satire more than real news [1]. The proliferation of misinformation can significantly affect issues for individuals and societies, influencing public health [2] and the stability of democracies [3; 4]. To tackle this issue, our research proposes a new approach using BT augmentation to improve the detection of fake news in Romanian. Considering the limitations in language resources and prior work predominantly focused on high-resource languages, this study provides significant contributions by adapting and examining the effectiveness of state-of-the-art transformer-based models, including mBART, in a less-studied linguistic context. Moving beyond conventional ML and DL strategies [5; 6; 7], we can position our work at the forefront of regional language misinformation analysis, which could inform future multilingual and cross-cultural misinformation combat strategies.  
Detecting fake news is a research topic of great interest in natural language processing (NLP) and artificial intelligence (AI). In recent years, most research on fake news detection has targeted English documents, with limited research in other languages [8]. However, studies have been conducted in Turkish, Indonesian, Chinese, Albanian, and Romanian [5; 9; 10]. It is critical to evaluate the language-independent characteristics and linguistic patterns that can be used to automatically detect fake news [1]. Additionally, attention-based models have been proposed to detect fake news in various languages [11]. The effectiveness of different algorithms and techniques, including ML and neural networks, in detecting fake news has been investigated [7; 12; 13]. Social media platforms, such as Twitter, where fake news can spread rapidly, must also be considered when detecting fake news [14; 15]. More research is needed to develop robust and language-independent methods for detecting fake news in Romanian and other languages.  
One of the common problems in the detection of fake news for low-resource languages involves difficulties in identifying high-quality sources of annotated data. Even if such sources are identified and a data set is acquired through rigorous data collection processes, it is inclined to be of a small size. Surveys such as those conducted by [PERSON] (16) have shown that data augmentation is helpful in addressing many challenges and problems for text classification. Such techniques are appropriate to increase the generalization of models and overcome a limited amount of training data.  
Paraphrasing is made easier by translation. Since machine translation models have advanced and APIs are available online, it has become a widely used augmentation method. In BT, the original text is translated into another language and then translated again to generate augmented text (17).  
This study investigates the effectiveness of BT augmentation, particularly transformer-based solutions, to enhance fake news detection in Romanian. A data set obtained from the fact checking site Factualro (accessed on 10 October 2023) was used in the experiment. The site publishes declarations of Romanian politicians labeled by a team of independent public policy specialists. Among the 1227 unique records in the Factualro data set, there are five categories: true, false, partially true, partially false, and truncated. To evaluate the effect of increasing the size of the data set with BT, multiple models were trained and evaluated.  
Related work is introduced in Section 2, while Section 3 outlines the experiment methodology. This includes the data set, augmentation techniques, preprocessing steps, classifiers, and evaluation procedure. Section 4 presents the results and a discussion of the key findings. Lastly, Section 5 outlines the findings and potential directions for future work.