  Automatic fact-checking systems detect misinformation, such as fake news, by
(i) selecting check-worthy sentences for fact-checking, (ii) gathering related
information to the sentences, and (iii) inferring the factuality of the
sentences. Most prior research on (i) uses hand-crafted features to select
check-worthy sentences, and does not explicitly account for the recent finding
that the top weighted terms in both check-worthy and non-check-worthy sentences
are actually overlapping [15]. Motivated by this, we present a neural
check-worthiness sentence ranking model that represents each word in a sentence
by \textit{both} its embedding (aiming to capture its semantics) and its
syntactic dependencies (aiming to capture its role in modifying the semantics
of other terms in the sentence). Our model is an end-to-end trainable neural
network for check-worthiness ranking, which is trained on large amounts of
unlabelled data through weak supervision. Thorough experimental evaluation
against state of the art baselines, with and without weak supervision, shows
our model to be superior at all times (+13% in MAP and +28% at various
Precision cut-offs from the best baseline with statistical significance).
Empirical analysis of the use of weak supervision, word embedding pretraining
on domain-specific data, and the use of syntactic dependencies of our model
reveals that check-worthy sentences contain notably more identical syntactic
dependencies than non-check-worthy sentences.
