  This paper describes a novel approach to systematically improve information
interactions based solely on its wording. Following an interdisciplinary
literature review, we recognized three key attributes of words that drive user
engagement: (1) Novelty (2) Familiarity (3) Emotionality. Based on these
attributes, we developed a model to systematically improve a given content
using computational linguistics, natural language processing (NLP) and text
analysis (word frequency, sentiment analysis and lexical substitution). We
conducted a pilot study (n=216) in which the model was used to formalize
evaluation and optimization of academic titles. A between-group design (A/B
testing) was used to compare responses to the original and modified (treatment)
titles. Data was collected for selection and evaluation (User Engagement
Scale). The pilot results suggest that user engagement with digital information
is fostered by, and perhaps dependent upon, the wording being used. They also
provide empirical support that engaging content can be systematically evaluated
and produced. The preliminary results show that the modified (treatment) titles
had significantly higher scores for information use and user engagement
(selection and evaluation). We propose that computational linguistics is a
useful approach for optimizing information interactions. The empirically based
insights can inform the development of digital content strategies, thereby
improving the success of information interactions.elop more sophisticated
interaction measures.
