Defending Democracy: Using Deep Learning to Identify and Prevent Misinformation

TL;DR

This paper presents a deep learning approach using BERT and propagation graphs to classify and visualize misinformation spread on Twitter, aiming to develop timely interventions to protect democratic discourse.

Contribution

It introduces a scalable BERT-based model for misinformation detection that improves precision and visualizes false content propagation on social media networks.

Findings

Confirmed user clustering and virality patterns of false content

Improved accuracy of misinformation classification

Demonstrated scalability of BERT for real-time detection

Abstract

The rise in online misinformation in recent years threatens democracies by distorting authentic public discourse and causing confusion, fear, and even, in extreme cases, violence. There is a need to understand the spread of false content through online networks for developing interventions that disrupt misinformation before it achieves virality. Using a Deep Bidirectional Transformer for Language Understanding (BERT) and propagation graphs, this study classifies and visualizes the spread of misinformation on a social media network using publicly available Twitter data. The results confirm prior research around user clusters and the virality of false content while improving the precision of deep learning models for misinformation detection. The study further demonstrates the suitability of BERT for providing a scalable model for false information detection, which can contribute to the development of more timely and accurate interventions to slow the spread of misinformation in online environments.