Hybrid Model Deep Learning for Fake News Detection

This document describes a hybrid deep learning model which implements CNNs with LSTMs for identifying fake news misinformation within the text of news articles. The spread of misinformation, referred to as ‘fake news’, is becoming increasingly prevalent on social media and traditional news sites, which in turn creates a need for automated systems designed to identify and flag false content. We suggest a hierarchical architecture where CNNs are effective in local feature extraction while LSTMs models adeptly harness long term dependencies (with LSTMs capturing an entire sequence). The model utilizes word embeddings as text input, then applies sequentially spatial dropout for regularization, convolutional layers for feature extraction, and stacked LSTM layers for sequence modeling. Our evaluation is conducted on a set of articles containing both true and false news published between 2015-2018. The approach achieved remarkable performance measured in accuracy, precision, recall, and F1 score. The results derived from the confusion matrices, ROC curves, and precision against recall through the confusion matrix evaluation, sharpen the conclusion drawn pertaining to the model’s ability to differentiate between genuine and fabricated news content. These findings indicate that the proposed hybrid architecture is an effective tool for automated fake news detection, confirm information authenticity Within the reality of a rapidly evolving media ecosystem.