In the recent years, digital wildfires, i.e. fast-spreading online misinformation have been identified as a considerable risk to developed societies, which raised the need for strategies to alleviate that risk. However, due to the speed with which online information spreads today, in combination with its immense volume, human monitoring of the Internet is completely infeasible, which gives rise to the need for an automated system. On the other hand, the requirements for such systems w.r.t. reliability, functionality, flexibility, and trustworthiness are immense. And while several approaches have been developed in the recent past, almost all of these attempts attack the problem purely from the technical side, generally using machine learning techniques. Our approach differs in that we study the problem from both sides, from the technical, but also form the human side by performing experiments and interviews aimed at understanding how people assess trustworthiness online, which content is likely to spread far, and why actors spread misinformation. On the technical side, the projects aims to design, implement, and deploy an IT system capable of analyzing large amounts of online news, focusing on Norwegian news sites and international sites that are cited frequently. The goal is to track where specific news items first appeared and how they spread, study the spread of misinformation, and, by using the knowledge gained from the experiments, enable the system to distinguish between misinformation and factual news. The overall objective is the prevention of digital wildfires via automated early warnings form the system, as well as enhanced preparedness for such events through intense study on the spread of such wildfires and the underlying reasons of the phenomenon.
Project leader: Johannes Langguth
Category: Øvrige forskningsinstitutter
Institution: SIMULA RESEARCH LABORATORY AS