Objective
In the past decade, social media have become one of the main sources of news for people around the world. Yet, it comes with the danger of exposure to intentionally false information. The extensive spread of fake news has recently become a centerpiece of controversy following the highly debated elections of President Donald Trump and the Brexit vote. It is alleged that the outcome of these votes resulted from the public opinion manipulation by a massive injection of fake news, possibly sponsored by hostile foreign governments, constituting perhaps one of the most serious and unprecedented threats to the modern democracies.
The ambition of GoodNews is to build the technological capability for algorithmic fake news detection in social media using a novel paradigm. Instead of following the traditional approach of analyzing the news content, we will analyze the news spreading patterns in social networks. The algorithmic core of GoodNews is based on a novel class of geometric deep learning algorithms developed in the ERC project LEMAN (Learning on Manifolds and Graphs). Our research group was among the first in the world to propose, implement, and patent generalizations of popular convolutional neural network architectures to graph-structured data such as social networks, allowing to do deep learning on graphs. The ability to learn fake news spread patterns on social networks will provide the needed breakthrough in the task of automated fake news detection.
GoodNews will convert the geometric deep learning technology into a commercial application of fake news detection in social media. The focus of the project will be three-fold: developing a demo system for fake news detection with real data from social media; verifying and solidifying our IP portfolio and its licensing terms; analyzing the market and coming up with a financeable business plan. We target establishing a company at the end of the project and attracting investment to develop a commercial-grade product.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences artificial intelligence machine learning deep learning
- natural sciences computer and information sciences artificial intelligence computational intelligence
You need to log in or register to use this function
We are sorry... an unexpected error occurred during execution.
You need to be authenticated. Your session might have expired.
Thank you for your feedback. You will soon receive an email to confirm the submission. If you have selected to be notified about the reporting status, you will also be contacted when the reporting status will change.
Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
-
H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC)
MAIN PROGRAMME
See all projects funded under this programme
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
ERC-POC - Proof of Concept Grant
See all projects funded under this funding scheme
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2018-PoC
See all projects funded under this callHost institution
Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
6900 LUGANO
Switzerland
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.