  To deal with the sheer volume of information and gain competitive advantage,
the news industry has started to explore and invest in news automation. In this
paper, we present Reuters Tracer, a system that automates end-to-end news
production using Twitter data. It is capable of detecting, classifying,
annotating, and disseminating news in real time for Reuters journalists without
manual intervention. In contrast to other similar systems, Tracer is topic and
domain agnostic. It has a bottom-up approach to news detection, and does not
rely on a predefined set of sources or subjects. Instead, it identifies
emerging conversations from 12+ million tweets per day and selects those that
are news-like. Then, it contextualizes each story by adding a summary and a
topic to it, estimating its newsworthiness, veracity, novelty, and scope, and
geotags it. Designing algorithms to generate news that meets the standards of
Reuters journalists in accuracy and timeliness is quite challenging. But Tracer
is able to achieve competitive precision, recall, timeliness, and veracity on
news detection and delivery. In this paper, we reveal our key algorithm designs
and evaluations that helped us achieve this goal, and lessons learned along the
way.
