# Enhancing Fake News Detection in Romanian Using Transformer-Based Back Translation Augmentation
## 3 Methodology
### Preprocessing  
The data preprocessing steps applied in this study involved data normalization, data cleaning, tokenization, stop words removal, lemmatization, and feature extraction. These steps aimed at cleaning and standardizing the textual data, making it suitable for additional analysis and modeling tasks.  
During the data normalization and cleaning phase, we employed specific functions to remove unnecessary characters from the text data. To ensure consistency and standardization, we initially utilized normalization techniques for special characters in Romanian.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Augmentation Type** & **Operation** & **Statement** \\ \hline None & - &
\begin{tabular}{} \end{tabular} \\ \hline Back Translation (Google Translate) & Translate to English & We are the only country that hospitalized people without any symptoms and we crowded the hospitals. We filled the hospitals with people who didn’t have any kind of problem. We are the only ones in Europe who have hospitalized people without any symptoms. \\  & Translate back to Romanian & Sumtem singura tara care a spitalizat personeale fara simptome si am algomerat spitalele. Am umplut spitalele cu oameni care nu aveau niciun fel de problema. Suntem singurii din Europa care au spitalizat persone fara simptome. \\ \hline Back Translation (mBART) & Translate to English & We are the only country that has hospitalized people without any symptoms and we have crowded the hospitals. We filled the hospitals with people who had no problem. We are the only people in Europe who have interned people without any symptoms. \\  & Translate back to Romanian & Sumtem singura tara care a spitalizat oameni fara niciun simptom si am am uplift spitalele. Am umplut spitalele cu oameni care nu au avut probleme. Suntem singurie persoane din Europa care au internat oameni fara niciun simptom. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statements resulting from BT using Google Translate and mBART.  
To further clean up the text, we remove digits, punctuation, and links using regular expressions. We also converted the text to lowercase to ensure consistency and prevent discrepancies resulting from case sensitivity.  
Tokenization was conducted to partition the text into single tokens. This process was carried out using the tokenizer from the Natural Language Toolkit (NLTK) library [27], which extracts alphanumeric tokens from the text. Stop words, which are commonly used words that do not carry significant meaning, were then removed from the tokenized text columns. This step helped in reducing noise and improving the quality of the data.  
Lemmatization was performed using the Stanza library [28], which reduces terms to their dictionary form. This step helped in reducing the dimensionality of the data and capturing the core meaning of the words. Finally, the lemmatized tokens in each column were joined into a single string, facilitating feature extraction and further analysis.  
Finally, we used Term Frequency-Inverse Document Frequency (TF-IDF) to convert the text data into a form that could be more effectively analyzed. TF-IDF is a strategy employed to compute the weight of a word in a document. The process starts with the computation of the frequency of every unique term in the text, which measures the frequency with which a word appears throughout the content.  
However, repeating a word many times in a document does not necessarily mean it is significant. To address this, we used the Inverse Document Frequency (IDF). IDF rescales common words across a corpus of documents--in this case, all articles analyzed for fake news detection. The IDF decreases the weight of commonly used words and increases the importance of terms not used very much in a collection of documents. This combination of TF and IDF offers us the TF-IDF score. This allows us to extract features from text data using TF-IDF, which can be used in ML models.