Computational fact checking

Misinformation is an important problem but fact checkers are overwhelmed by the amount of false content that is produced online every day. To assist human experts in their efforts, several ongoing projects are proposing computational methods that aim at supporting the different steps in the fact-checking pipeline, from claim detection to their verification. In the first part of the lecture, we will overview the different approaches for the different steps, spanning from solutions involving humans and a crowd of users to fully automated approaches. In the second part, we will focus our attention on the data driven verification methods that use reference information to assess claims. We will review methods that combine solutions from the ML and NLP literature to build data driven verification, such as those that translate text claims into SQL queries on relational databases. We will also cover how the rich semantics in knowledge graphs (KGs) can be used to verify claims and produce explanations, which is a key requirement in this space. Better access to data and new algorithms are pushing computational fact checking forward, with experimental results showing that verification methods enable effective labeling of claims, both in simulations and in real world efforts such as https://coronacheck.eurecom.fr. However, while fact checkers start to adopt some of the resulting tools, the misinformation fight is far from being won. In the last part of this lecture, we will cover the opportunities and limitations of computational fact checking and its role in fighting misinformation.