  We contribute the largest publicly available dataset of naturally occurring
factual claims for the purpose of automatic claim verification. It is collected
from 26 fact checking websites in English, paired with textual sources and rich
metadata, and labelled for veracity by human expert journalists. We present an
in-depth analysis of the dataset, highlighting characteristics and challenges.
Further, we present results for automatic veracity prediction, both with
established baselines and with a novel method for joint ranking of evidence
pages and predicting veracity that outperforms all baselines. Significant
performance increases are achieved by encoding evidence, and by modelling
metadata. Our best-performing model achieves a Macro F1 of 49.2%, showing that
this is a challenging testbed for claim veracity prediction.
