Fake news, Digital Wildfires, and Graph Neural Networks

In the wake of the 2016 US presidential elections, misinformation in social networks, often referred to as fake news, has received significant attention from media, researchers, and the general public.

We introduce the the topic while focusing on digital wildfires i.e. fast spreading online misinformation phenomena with the potential to cause harm in the physical world.

We introduce possible countermeasures from the computer science point of view and show that among the possible countermeasures, network based approaches are particularly suitable, both due to their generality and robustness.

We then explain the recently developed graph neural networks and show why they constitute a powerful tool for network based tracking of misinformation.

Trial lecture from 11:15-12.

Technical session from 12:30-14:30.