The Language of Dialogue Is Complex
This enables large-scale social media analytics for IC, which is incremental as it automates an existing manual method.
The authors tackled the problem of automating Integrative Complexity (IC) scoring, a time-consuming manual process, by developing an NLP and machine learning model that achieved state-of-the-art performance on unseen data and replicated prior findings when applied to over 400,000 online comments.
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.