# Romanian Fake News Detection Using Machine Learning and Transformer-Based Approaches
## 2 State of the Art
The study in [13] tackles this issue by proposing a framework that employs emotion-based sentiment analysis to analyze news, posts, and opinions on social media. The model computes sentiment scores to detect fake or spam content and identify both accounts, achieving an accuracy of 99.68%, surpassing other methods. In [14], a new solution is proposed for fake news detection, incorporating sentiment as an important feature to improve the accuracy; it is trained on two datasets: LIAR and ISOT [15]. The study develops key feature words using sentiment analysis with a lexicon-based scoring algorithm. A multiple imputation strategy, based on Multiple Imputation by Chained Equations (MICE), addresses missing variables in the dataset. Term frequency-inverse document frequency (TF-IDF) is used to extract long-term features, and classifiers such as Naive Bayes, passive-aggressive, and deep neural networks (DNNs) are employed to categorize data. The method achieved 99.8% accuracy in detecting fake news, outperforming existing methods when evaluated on statements ranging from true to false. Although previous research on automatic fake news detection has been conducted for other languages, work in this area for Romanian is still in its early stages. Ref. [16] presents an automated analysis of political statements in Romanian using state-of-the-art natural language processing techniques, focusing on the role of context in determining veracity. The dataset is based on entries from Factual, a Romanian fact-checking initiative, which includes public statements along with contextual information. The results are comparable to experiments on the PolitiFact dataset and serve as a solid baseline for studies in low-resource languages like Romanian. The research presented in [17] discusses state-of-the-art techniques for text classification and evaluates the performance of different neural networks on a Romanian news article corpus. Both classical machine learning methods and more advanced Transformer-based models were considered, with RoBERTa and CNN achieving a weighted F1 score of 75%. Multi-task learning experiments, while promising, did not improve performance, reaching an F1 score of 74%. Additionally, the paper introduces a prototype web application and explores use cases for automated fake news detection systems. The work in [18] focuses on the automatic detection of fake news in the Romanian language, aiming to improve accuracy in identifying deceptive content. It introduces a new available dataset for fake news detection, consisting of two subsets containing 977 and 29,154 Romanian news articles, categorized based on labeling and collection methods. The study explores various text-based approaches using artificial intelligence and machine learning, achieving an accuracy of 93%. Ref. [19] introduces a supervised machine learning system designed to detect fake news in Romanian online sources. It compares the performance of various models, including recurrent neural networks (LSTM--long short-term memory; and GRU--gatedrecurrent unit cells), a convolutional neural network (CNN), and the RoBERT model, a pre-trained BERT variant for Romanian. These deep learning models are evaluated against traditional classifiers like Naive Bayes and Support Vector Machine (SVM) using a dataset with 25,841 true and 13,064 fake news items. CNN achieves the best performance, with over 98.20% accuracy, surpassing both classical methods and BERT models. Additionally, the study explores irony detection and sentiment analysis to address fake news challenges. In [20], the authors employ three key approaches for attaining smaller and more efficient NLP models. The technique involves training a Transformer-based neural network to detect offensive language. Data augmentation and knowledge distillation techniques are used in order to increase performance. Also, multi-task learning with knowledge distillation and teacher annealing is incorporated.