  Information quality in social media is an increasingly important issue, but
web-scale data hinders experts' ability to assess and correct much of the
inaccurate content, or `fake news,' present in these platforms. This paper
develops a method for automating fake news detection on Twitter by learning to
predict accuracy assessments in two credibility-focused Twitter datasets:
CREDBANK, a crowdsourced dataset of accuracy assessments for events in Twitter,
and PHEME, a dataset of potential rumors in Twitter and journalistic
assessments of their accuracies. We apply this method to Twitter content
sourced from BuzzFeed's fake news dataset and show models trained against
crowdsourced workers outperform models based on journalists' assessment and
models trained on a pooled dataset of both crowdsourced workers and
journalists. All three datasets, aligned into a uniform format, are also
publicly available. A feature analysis then identifies features that are most
predictive for crowdsourced and journalistic accuracy assessments, results of
which are consistent with prior work. We close with a discussion contrasting
accuracy and credibility and why models of non-experts outperform models of
journalists for fake news detection in Twitter.
