  Integrative Complexity (IC) is a psychometric that measures the ability of a
person to recognize multiple perspectives and connect them, thus identifying
paths for conflict resolution. IC has been linked to a wide variety of
political, social and personal outcomes but evaluating it is a time-consuming
process requiring skilled professionals to manually score texts, a fact which
accounts for the limited exploration of IC at scale on social media.We combine
natural language processing and machine learning to train an IC classification
model that achieves state-of-the-art performance on unseen data and more
closely adheres to the established structure of the IC coding process than
previous automated approaches. When applied to the content of 400k+ comments
from online fora about depression and knowledge exchange, our model was capable
of replicating key findings of prior work, thus providing the first example of
using IC tools for large-scale social media analytics.

Computers and Society;Computation and Language;Social and Information Networks