  The unmoderated nature of social media enables the diffusion of hoaxes, which
in turn jeopardises the credibility of information gathered from social media
platforms. Existing research on automated detection of hoaxes has the
limitation of using relatively small datasets, owing to the difficulty of
getting labelled data. This in turn has limited research exploring early
detection of hoaxes as well as exploring other factors such as the effect of
the size of the training data or the use of sliding windows. To mitigate this
problem, we introduce a semi-automated method that leverages the Wikidata
knowledge base to build large-scale datasets for veracity classification,
focusing on celebrity death reports. This enables us to create a dataset with
4,007 reports including over 13 million tweets, 15% of which are fake.
Experiments using class-specific representations of word embeddings show that
we can achieve F1 scores nearing 72% within 10 minutes of the first tweet being
posted when we expand the size of the training data following our
semi-automated means. Our dataset represents a realistic scenario with a real
distribution of true, commemorative and false stories, which we release for
further use as a benchmark in future research.
