Paper Title
A Deep Learning-Based Approach for Fake News Detection in a Mobile Application

Abstract
The proliferation of social media and online news platforms has led to a surge in the dissemination of "fake news," which has significant negative consequences for society. This paper proposes a deep learning-based approach for detecting fake news through a mobile application. The proposed system utilizes a hybrid model that combines a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) network to effectively capture both local and sequential features from news articles. The mobile application provides a user-friendly interface for users to input a news article's URL or text, and the backend server, powered by the CNN-LSTM model, classifies the news as either real or fake. The proposed system is evaluated on a publicly available dataset, and the experimental results demonstrate its effectiveness in detecting fake news with high accuracy. Keywords - Fake News Detection, Deep Learning, CNN, LSTM, Mobile Application, Natural Language Processing