Funding company

FightDIS project aims to understand how false information, or “information disorders” (such as fake news, hoaxes, and rumors), spreads within online social networks and how it impacts public opinion. Information disorders have affected areas like health, politics, and social events, with notable examples including the anti-vaccination movement and the spread of false information during events like the 2016 USA elections, Brexit, and the COVID-19 pandemic. This growing issue has driven researchers from various fields (psychology, sociology, communication, etc.) to investigate factors such as confirmation bias and the echo-chamber effect, both of which reinforce the spread of disinformation by isolating individuals in ideologically homogeneous groups.

The project’s main goal is to study how different types of actors within online networks contribute to the dissemination of false information. Using machine learning and data mining, the project will identify social networks on various platforms that promote these disorders and analyze their patterns of behavior. By considering multiple forms of data (text, video, audio, etc.) and temporal activity, the research will provide tools and algorithms to track disinformation sources, understand the dynamics of information spread, and counteract future disinformation campaigns. The project will test its techniques in domains like health, extremism, politics, and violent events, adhering to Open-Science principles to maximize its societal and scientific impact.