Project description
Just the facts, please!
Journalism is about disseminating facts. As such, a reporter’s job is to check the facts being presented to the public. In recent years, the verification of social media content has become increasingly important to journalists and news organisations. The proliferation of social media means a larger volume of claims to verify; therefore, automated verification methods could help journalists assess the truthfulness of claims. The EU-funded AVeriTeC project will use machine learning approaches to develop an automated verification system that can process complex claims that require multiple pieces of evidence to cross-check. AVeriTeC’s ultimate goal is to establish the verification of textual claims as a real-world challenge to stimulate progress in natural language processing, machine learning and related fields.
Objective
Verification of textual claims is the task of assessing the truthfulness of a statement in natural language. It is commonly conducted manually by journalists on claims made by public figures such as politicians, with the aim of reducing misinformation. However, the proliferation of social media has created the need to apply verification to a larger volume of claims coming from a greater variety of sources, thus calling for automation.
Research in automated verification of textual claims is at an early stage. The methods developed either assess the truthfulness of the claim without considering evidence, or handle very simple claims such as “UK has 3.2 million EU immigrants” that requires the retrieval of a single factoid from a knowledge base. While useful, claims are often more complex, and taking evidence into account is necessary for the verdicts to be credible.
AVeriTeC will transform automated verification by enabling the verification of more complex claims than previously attempted, such as “the United Kingdom has ten times Italy’s number of immigrants”, which require multiple pieces of evidence. We will achieve this by developing methods able to generate multiple questions per claim, retrieve answers from both knowledge bases and textual sources, and combine them into verdicts. As these tasks are interdependent, we will develop novel machine learning approaches able to handle them jointly so that the verdicts are accompanied by suitable justifications in the form of questions and answers. The latter will be formulated in natural language, thus the process followed by the models developed will be explainable to the users, while the evidence itself can be useful even if the overall verdict is incorrect.
Beyond developing novel methods and creating publicly available evaluation resources, AVeriTeC will establish verification of textual claims as a real-world challenge to stimulate progress in natural language processing, machine learning and related fields.
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.
- social sciences media and communications journalism
- social sciences sociology industrial relations automation
- social sciences sociology demography human migrations
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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.
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H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC)
MAIN PROGRAMME
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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-COG - Consolidator Grant
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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-2019-COG
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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.
CB2 1TN CAMBRIDGE
United Kingdom
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.