# Romanian Fake News Detection Using Machine Learning and Transformer-Based Approaches
## 4 Results
### Our Application  
The application is designed to detect fake news using the best-performing AI model that resulted from the research. Built with a modern tech stack, the application leverages the power of Next.js for server-side rendering, React for building user interfaces, and Prisma for database management. The project provides an intuitive and interactive platform for users to analyze and visualize the performance of machine learning models tested in our research in detecting fake news. 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 key features of our implementation include the following:  
* Fake news detection: The core functionality of the application is to detect fake news using multiple machine learning models. Users can input text or URLs, and the application will classify the content.
* Model evaluation: The application provides detailed evaluations of different machine learning models, including metrics such as accuracy, precision, recall, F1 score, and log loss. These metrics help users understand the performance of each model tested during the research.
* Data visualization: The application includes rich data visualization features, allowing users to compare the performance of different models through charts and tables. This is implemented using the Recharts library.
* Interactive UI: The user interface is built with Radix UI and Tailwind CSS, providing a responsive and accessible design. Users can easily navigate through different tabs to view model evaluations and dataset statistics.
* Authentication: The application uses NextAuth.js for secure user authentication, ensuring that only authorized users can access certain features.
* Database management: Prisma is used for database management, providing a robust and scalable solution for handling dataset entries.  
The technologies used to implement the application include Next.js--a React framework for server-side rendering and static site generation; React--a JavaScript library for building user interfaces; TypeScript--a typed superset of JavaScript that enhances code quality and maintainability; Prisma--an ORM for Node.js and TypeScript, used for database management; Radix UI--a set of accessible UI components for building design systems; Recharts--a charting library built on React components for data visualization; Tailwind CSS--a utility-first CSS framework for rapidly building custom user interfaces; NextAuth.js--authentication for Next.js applications; Zod--a TypeScript-first schema declaration and validation library; Lucide Icons--a collection of simple and customizable SVG icons; React Hook Form--a library for managing form state and validation in React applications; TanStack Table--a headless UI library for building powerful tables and datgrids; and React Code Blocks--a library for rendering code blocks with syntax highlighting.  
In the input process, users can add text or URLs to be analyzed by the AI models. Then, the input is processed by the selected machine learning model, which classifies the content as real or fake. The results are displayed to the user, along with detailed metrics and visualizations of the model's performance.