  Our society produces and shares overwhelming amounts of information through
Online Social Networks (OSNs). Within this environment, misinformation and
disinformation have proliferated, becoming a public safety concern in most
countries. Allowing the public and professionals to efficiently find reliable
evidences about the factual veracity of a claim is a crucial step to mitigate
this harmful spread. To this end, we propose FacTeR-Check, a multilingual
architecture for semi-automated fact-checking that can be used for either
applications designed for the general public and by fact-checking
organisations. FacTeR-Check enables retrieving fact-checked information,
unchecked claims verification and tracking dangerous information over social
media. This architectures involves several modules developed to evaluate
semantic similarity, to calculate natural language inference and to retrieve
information from Online Social Networks. The union of all these components
builds a semi-automated fact-checking tool able of verifying new claims, to
extract related evidence, and to track the evolution of a hoax on a OSN. While
individual modules are validated on related benchmarks (mainly MSTS and SICK),
the complete architecture is validated using a new dataset called NLI19-SP that
is publicly released with COVID-19 related hoaxes and tweets from Spanish
social media. Our results show state-of-the-art performance on the individual
benchmarks, as well as producing a useful analysis of the evolution over time
of 61 different hoaxes.

Computation and Language