  Many news outlets allow users to contribute comments on topics about daily
world events. News articles are the seeds that spring users' interest to
contribute content, i.e., comments. An article may attract an apathetic user
engagement (several tens of comments) or a spontaneous fervent user engagement
(thousands of comments). In this paper, we study the problem of predicting the
total number of user comments a news article will receive. Our main insight is
that the early dynamics of user comments contribute the most to an accurate
prediction, while news article specific factors have surprisingly little
influence. This appears to be an interesting and understudied phenomenon:
collective social behavior at a news outlet shapes user response and may even
downplay the content of an article. We compile and analyze a large number of
features, both old and novel from literature. The features span a broad
spectrum of facets including news article and comment contents, temporal
dynamics, sentiment/linguistic features, and user behaviors. We show that the
early arrival rate of comments is the best indicator of the eventual number of
comments. We conduct an in-depth analysis of this feature across several
dimensions, such as news outlets and news article categories. We show that the
relationship between the early rate and the final number of comments as well as
the prediction accuracy vary considerably across news outlets and news article
categories (e.g., politics, sports, or health).
