Binary Classification of News Articles using Deep Learning

The proliferation of fake news has emerged as a substantial concern in the contemporary digital era. The advent of social media and online news platforms has significantly facilitated the dissemination of misinformation and propaganda. Consequently, there is a pervasive scepticism surrounding conventional news outlets, influencing political outcomes. Thus, it becomes imperative to devise robust strategies for the detection of fake news. In recent times, deep learning has proven to be a potent tool, particularly in the domain of natural language processing.This scholarly paper introduces an inventive deep learning-based methodology for identifying fake news. The proposed model integrates Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (LSTM) networks, and the transformer-based model BERT to adeptly extract features from textual content within news articles. Through training on a substantial dataset of categorized news articles and evaluation using various metrics, the experimental outcomes compellingly affirm the effectiveness of the proposed approach in accurately discerning fake news, achieving high precision and recall rates. This methodology holds considerable promise in counteracting the proliferation of misinformation on social media and online news platforms