# Romanian Fake News Detection Using Machine Learning and Transformer-Based Approaches
## 2 State of the Art  
In recent years, fake news detection has become increasingly important due to the widespread dissemination of misleading information across social media and news platforms. Artificial intelligence (AI) has proven to be a key enabler in identifying and mitigating the spread of such content, using various machine learning (ML) and natural language processing (NLP) techniques. The study presented in [4] provides an overview of traditional machine learning models, including Naive Bayes and Logistic Regression, applied to fake news detection, discussing their strengths and weaknesses. Ref. [5] categorizes fake news and discusses the use of traditional models like Logistic Regression and Naive Bayes for linguistic feature-based deception detection. The research presented in [6] investigates the use of traditional machine learning models, such as SVM and Naive Bayes, for fake news detection, focusing on n-gram linguistic features. Utilizing ML classification models offers an effective solution to combat the spread of fake news. However, the development of fake news detection models has been hindered by the lack of comprehensive datasets. Existing datasets are limited in their scope, often missing multimodal text and image data, metadata, comments, and detailed fake news categorization. In response, in [7], Fakeddit is introduced, a novel multimodal dataset containing over 1 million samples across various fake news categories. After undergoing multiple review stages, the samples are labeled for 2-way, 3-way, and 6-way classification using distant supervision. Hybrid text and image models are developed by the authors and extensive experiments are conducted, showcasing the value of multimodality and fine-grained classification that are unique to Fakeddit. Ref. [8] presents an extensive review of deep learning-based fake news detection (FND) methods, highlighting critical features like news content, social context, and external knowledge. The methods are grouped into supervised, weakly supervised, and unsupervised categories, with a detailed evaluation of representative approaches. The study also introduces commonly used FND datasets and provides a quantitative performance analysis of these methods across the datasets. Finally, it addresses the limitations of current techniques and proposes promising directions for future research. Transformers, built on attention mechanisms, are powerful models for natural language processing tasks like text generation and language understanding. Pre-trained models like BERT and GPTs (Generative Pre-trained Transformers) have achieved state-of-the-art results in AI competitions and practical applications [9]. These advanced deep learning models have revolutionized AI, pushing the boundaries in fields like computer vision, natural language processing, and more. Each model has unique features and is chosen based on specific problem requirements. The study presented in [10] introduces the Transformer model, which has since become crucial in NLP tasks, including fake news detection. In [11], BERT is introduced as a new language representation model. BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. BERT model is conceptually simple and powerful, and the pre-trained BERT model has the huge advantage of being fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, including fake news detection. Social networking platforms have become major hubs for global users to share views, news, and information. The rise of fake and spam profiles spreading disinformation has become a growing concern. While efforts have been made to distinguish real news from fake campaigns, manual verification of the vast amount of social media content is impractical. A detailed survey of fake news detection approaches, focusing on both content-based methods and social-context-based models is given in [12].