Fake news detection System

Abstract

Artificial Intelligence (AI) refers to the study of algorithms and computational methods that enable machines to perform tasks that traditionally require human intelligence. It represents a branch of computer science that focuses on developing systems capable of learning, reasoning, and making decisions. In recent years, researchers worldwide have shown increasing interest in the problem of fake news detection, particularly due to the rapid spread of misinformation through digital platforms. Several studies in the social sciences have examined the impact of false information and how individuals perceive and respond to misleading content. Fake news generally refers to information that is intentionally inaccurate or misleading and designed to influence the beliefs or opinions of its audience. Data mining classification techniques play an important role in detecting such misleading information. These methods rely on machine learning algorithms that incorporate mathematical models, probability theory, statistical distributions, and artificial intelligence principles. A rapidly growing area within machine learning is deep learning, which involves multiple layers of neural networks that perform nonlinear transformations to extract complex patterns and representations from data. Before applying these models, data preprocessing is an essential step that addresses various challenges in raw datasets. Preprocessing tasks include operations such as data cleaning, data integration, user and session identification, and noise removal. In this review paper, we explore the concepts of artificial intelligence, data preprocessing, fake news detection, and deep learning while also discussing different classification techniques commonly used in this domain.