  We introduce the Constructive Comments Corpus (C3), comprised of 12,000
annotated news comments, intended to help build new tools for online
communities to improve the quality of their discussions. We define constructive
comments as high-quality comments that make a contribution to the conversation.
We explain the crowd worker annotation scheme and define a taxonomy of
sub-characteristics of constructiveness. The quality of the annotation scheme
and the resulting dataset is evaluated using measurements of inter-annotator
agreement, expert assessment of a sample, and by the constructiveness
sub-characteristics, which we show provide a proxy for the general
constructiveness concept. We provide models for constructiveness trained on C3
using both feature-based and a variety of deep learning approaches and
demonstrate that these models capture general rather than topic- or
domain-specific characteristics of constructiveness, through domain adaptation
experiments. We examine the role that length plays in our models, as comment
length could be easily gamed if models depend heavily upon this feature. By
examining the errors made by each model and their distribution by length, we
show that the best performing models are less correlated with comment
length.The constructiveness corpus and our experiments pave the way for a
moderation tool focused on promoting comments that make a contribution, rather
than only filtering out undesirable content.
