# Romanian Fake News Detection Using Machine Learning and Transformer-Based Approaches
## 6 Conclusions  
This study investigated the performance of both traditional machine learning models and Transformer-based approaches for Romanian fake news detection. The results demonstrate that RoBERTa achieved the highest accuracy of 94.4%, while BERT followed closely with 92.5%. Among the traditional models, Logistic Regression performed the best, achieving an accuracy of 84.3%. These findings highlight the effectiveness of Transformer-based models in capturing the linguistic nuances of Romanian, particularly in the context of fake news classification.  
However, the proposed approach has several limitations. First, the lack of extensive annotated datasets in Romanian poses a challenge for training and evaluating models comprehensively. Second, Romanian-specific linguistic features, such as diacritics and flexible word order, may impact model performance and require further analysis. Lastly, the high computational cost of Transformer models could limit their scalability for large-scale applications. Future work should focus on expanding the dataset, exploring data augmentation techniques, and fine-tuning models to better account for the unique characteristics of the Romanian language.  
Conceptualization, E.V.M. and D.E.P.; methodology, E.V.M., D.E.P. and B.C.M.; software, B.C.M.; validation, B.C.M., E.V.M., D.E.P., S.M.C. and A.M.P.; formal analysis, E.V.M., D.E.P. and B.C.M.; investigation, E.V.M. and B.C.M.; resources, E.V.M., D.E.P. and B.C.M.;data curation, E.V.M., D.E.P. and B.C.M.; writing--original draft preparation, E.V.M., S.M.C. and A.M.P.; writing--review and editing, E.V.M., S.M.C., D.E.P., A.M.P. and B.C.M.; visualization, D.E.P.; supervision, D.E.P. and E.V.M.; project administration, D.E.P. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by the University of Oradea.  
Not applicable.  
The data supporting the reported results can be found at [[https://ai-fake-news-ro.vercel.app/research#model_evaluation](https://ai-fake-news-ro.vercel.app/research#model_evaluation)]([https://ai-fake-news-ro.vercel.app/research#model_evaluation](https://ai-fake-news-ro.vercel.app/research#model_evaluation)) (accessed on 15 December 2024). The NEW dataset (enhanced with new articles, before preprocessing) is available at [[https://huggingface.co/datasets/mihalca/FakeRom_updated_original](https://huggingface.co/datasets/mihalca/FakeRom_updated_original)]([https://huggingface.co/datasets/mihalca/FakeRom_updated_original](https://huggingface.co/datasets/mihalca/FakeRom_updated_original)) (accessed on 15 December 2024). The NEW dataset (enhanced with new articles, after preprocessing) is available at [[https://huggingface.co/datasets/mihalca/FakeRO_updated](https://huggingface.co/datasets/mihalca/FakeRO_updated)]([https://huggingface.co/datasets/mihalca/FakeRO_updated](https://huggingface.co/datasets/mihalca/FakeRO_updated)) (accessed on 15 December 2024). The application can be accessed at [[https://ai-fake-news-ro.vercel.app/](https://ai-fake-news-ro.vercel.app/)]([https://ai-fake-news-ro.vercel.app/](https://ai-fake-news-ro.vercel.app/)) (accessed on 15 December 2024).  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AI & Artificial intelligence \\ SVM & Support Vector Machine \\ BERT & Bidirectional Encoder Representations from Transformers \\ RoBERT & Pre-trained BERT model for Romanian \\ ML & Machine learning \\ NLP & Natural language processing \\ FND & Fake news detection \\ GPT & Generative Pre-trained Transformer \\ MICE & Multiple Imputation by Chained Equations \\ TF-IDF & Term Frequency-Inverse Document Frequency \\ DNN & Deep neural network \\ LSTM & Long short-term memory \\ GRU & Gated recurrent unit \\ BT & Back translation \\ DL & Deep learning \\ JSON & JavaScript Object Notation \\ \end{tabular}