Sticky words: Evaluation and optimization of information interactions based on linguistic analysis

Abstract

This paper describes a novel approach to systematically improve interactions with digital content based solely on its wording. Following an interdisciplinary literature review, we recognized three key attributes of words that drive successful interactions: (1) Novelty (2) Familiarity (3) Sentimentality. Based on these attributes, we developed a model to systematically improve a given content using word frequency, sentiment analysis and semantic substitution, and by employing computational linguistics and natural language processing (NLP) techniques. We conducted a pilot study (n=416) in which the model was used to formalize evaluation and optimization of academic titles. A between-group design was used to compare responses to the original (control) and modified (treatment) titles. Results show that the modified titles had significantly higher scores for selection, user engagement and memorability. Our findings suggest that users’ successful interactions with digital content 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. Implications and future research directions are discussed.