Project description
Distributed computing to rub out fake news
The fight against fake news is real. People worldwide are exposed to large-scale disinformation, including misleading or false information. The European Commission has made it a priority to tackle the spread and impact of online disinformation (defined as ‘verifiably false or misleading information created, presented and disseminated for economic gain or to intentionally deceive the public’). It may cause public harm, threaten democracies, and put EU citizens’ health, security and environment at risk. The EU-funded FARE project will develop a strategy based on distributed computing as well as guidelines for the ethical handling of human-related Big Data. It will answer the following questions: What’s the role of cognitive biases? How does network architecture affect the strength of fake news?
Objective
Recent events, from the anti-vaccination movement, to Brexit and even to mob killings, have raised serious concerns about the influence of the so-called fake news (FN). False information is not new in human history, but the recent surge in online activity, coupled with poor digital literacy, consumer profiling, and large profits from ad revenues, created a perfect storm for the FN epidemic, with still unimaginable consequences.
This challenge is interdisciplinary and requires academic research to guide current calls for action issued by academics, governmental and non-governmental agencies, and the social network platforms themselves. FARE will enrich current efforts, which mostly confront FN spreading from an applied perspective, by offering a theoretical framework that allows to make testable predictions. FARE argues that sharing of FN is a deviation from pure rationality and brings together 1) state of the art knowledge in behavioural psychology, to assess the role that cognitive biases play in susceptibility to FN, and 2) current models in network science and epidemiology, to test whether FN spread more like simple or complex contagions. Finally, fully recognizing that these novel big-data approaches carry great risks, FARE will develop a new strategy, mostly based on distributed computing, and guidelines to the ethical handling of human-related big-data.
Together, FARE will offer a comprehensive model to ask questions such as: 1) What role(s) cognitive biases play in FN spreading? 2) How does network architecture affect FNs spread? 3) How do biases and position on networks build on each other to impact propagation? 4) What monitoring and mitigation interventions are likely to be more efficient?
Moreover, the study of FN from such a conceptual perspective has the potential to profoundly increase our knowledge on human behaviour and information spread, beyond specific problems, with implications for communication (science, political), economics, and psychology.
Fields of science
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Funding Scheme
ERC-STG - Starting GrantHost institution
3004 516 Coimbra
Portugal