  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.

Social and Information Networks;Artificial Intelligence;Computation and Language;Machine Learning