The Role of Context in Detecting Previously Fact-Checked Claims
release_75ej5qtby5hqvhm6aw7p72gjhq
by
Shaden Shaar, Firoj Alam, Giovanni Da San Martino, Preslav Nakov
2022
Abstract
Recent years have seen the proliferation of disinformation and fake news
online. Traditional approaches to mitigate these issues is to use manual or
automatic fact-checking. Recently, another approach has emerged: checking
whether the input claim has previously been fact-checked, which can be done
automatically, and thus fast, while also offering credibility and
explainability, thanks to the human fact-checking and explanations in the
associated fact-checking article. Here, we focus on claims made in a political
debate and we study the impact of modeling the context of the claim: both on
the source side, i.e., in the debate, as well as on the target side, i.e., in
the fact-checking explanation document. We do this by modeling the local
context, the global context, as well as by means of co-reference resolution,
and multi-hop reasoning over the sentences of the document describing the
fact-checked claim. The experimental results show that each of these represents
a valuable information source, but that modeling the source-side context is
most important, and can yield 10+ points of absolute improvement over a
state-of-the-art model.
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