DATA TALK:"Explainable Fact Checking for Statistical and Property Claims"

Paolo Papotti (EURECOM, Data Science) -
Data Science

Date: May 27th 2021
Location: Eurecom - Eurecom

Abstract: Fact checkers are overwhelmed by the amount of false content that is produced online every day. To support fact checkers in their efforts, we are creating data driven verification methods that use structured datasets to assess claims and explain their decisions. For statistical claims, we translate text claims into SQL queries on relational databases. For property claims, we use the rich semantics in knowledge graphs (KGs) to verify claims and produce explanations. Experiments show that both methods enable the efficient and effective labeling of claims with interpretable explanations, both in simulations and in real world user studies with 50% decrease in verification time. Our algorithms are demonstrated in a fact checking website (, which has been used by more than twelve thousands users to verify claims related to the coronavirus disease (COVID-19) spreads and effects. References: Georgios Karagiannis, Mohammed Saeed, Paolo Papotti, Immanuel Trummer: Scrutinizer: A Mixed-Initiative Approach to Large-Scale, Data-Driven Claim Verification. Proc. VLDB Endow. 13(11): 2508-2521 (2020) Naser Ahmadi, Joohyung Lee, Paolo Papotti, Mohammed Saeed: Explainable Fact Checking with Probabilistic Answer Set Programming. TTO 2019 Bio: Paolo Papotti is an Associate Professor at EURECOM, France since 2017. He got his PhD from Roma Tre University (Italy) in 2007 and had research positions at the Qatar Computing Research Institute (Qatar) and Arizona State University (USA). His research is focused on data integration and information quality. He has authored more than 100 publications, and his work has been recognized with two “Best of the Conference” citations (SIGMOD 2009, VLDB 2016), two best demo award (SIGMOD 2015, DBA 2020), and two Google Faculty Research Award (2016, 2020). He is associate editor for PVLDB and the ACM Journal of Data and Information Quality (JDIQ). Data Science Seminars: (internal)

Permalink: https://www.eurecom/seminar/101335