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The rise in online misinformation in recent years threatens democracies bydistorting authentic public discourse and causing confusion, fear, and even, inextreme cases, violence. There is a need to understand the spread of falsecontent through online networks for developing interventions that disruptmisinformation before it achieves virality. Using a Deep BidirectionalTransformer for Language Understanding (BERT) and propagation graphs, thisstudy classifies and visualizes the spread of misinformation on a social medianetwork using publicly available Twitter data. The results confirm priorresearch around user clusters and the virality of false content while improvingthe precision of deep learning models for misinformation detection. The studyfurther demonstrates the suitability of BERT for providing a scalable model forfalse information detection, which can contribute to the development of moretimely and accurate interventions to slow the spread of misinformation inonline environments.

Defending Democracy: Using Deep Learning to Identify and Prevent Misinformation