Deep Learning–Based Fake News Detection System

Abstract

Fake news dissemination on social media platforms has become a serious societal problem, affecting public opinion, elections, and public health. Traditional methods relying on manual verification are slow and often ineffective. In recent years, deep learning techniques have shown promise in automating the detection of fake news by analyzing textual content and patterns. This paper proposes a deep learning–based approach for fake news detection using natural language processing (NLP) techniques. The model employs a combination of word embeddings and a bidirectional Long Short-Term Memory (Bi-LSTM) network to classify news articles as real or fake. Experimental results demonstrate that the proposed model achieves high accuracy, precision, and recall, outperforming traditional machine learning methods. The system can assist social media platforms and news agencies in identifying and limiting the spread of misinformation.