Using Attention-Based Models to Automate Fake News Detection.

Fake news involves disseminating deceptive, offensive, and inaccurate information, masquerading as credible news. Its primary objective is to tarnish individuals' public reputations while profiting from advertisements built on false content. We introduce an efficient fake news detection system using binary classification, employing attention-based Recurrent Neural Network (RNN) models. Our unified model, characterized by superior accuracy and comprehension, undergoes rigorous evaluation with an internet-sourced dataset, consistently yielding top-tier performance metrics. Despite relying on a single model for the entire detection process, it maintains end-to-end optimization flexibility, minimizes errors, and effectively balances bias-variance trade-offs. Our study explores fake news articles, their creators, and detection challenges, identifying heterogeneous social network patterns, both overt and covert, frequently exploited by fake news creators. We introduce a Deep Diffusive Network Model incorporating network structural data into learning. Our straightforward, linear, low-latency system achieves 98% accuracy in fake news detection. In addition to modeling and detection, advanced data analysis techniques yield deeper dataset insights.