Words That Stick: Predicting Decision Making and Synonym Engagement
  Using Cognitive Biases and Computational Linguistics
Nimrod Dvir Elaine Friedman Suraj Commuri Fan Yang Jennifer Romano
This research draws upon cognitive psychology and information systems studies
to anticipate user engagement and decision-making on digital platforms. By
employing natural language processing (NLP) techniques and insights from
cognitive bias research, we delve into user interactions with synonyms within
digital content. Our methodology synthesizes four cognitive
biasesRepresentativeness, Ease-of-use, Affect, and Distributioninto the READ
model. Through a comprehensive user survey, we assess the model's ability to
predict user engagement, discovering that synonyms that accurately represent
core ideas, are easy to understand, elicit emotional responses, and are
commonly encountered, promote greater user engagement. Crucially, our work
offers a fresh lens on human-computer interaction, digital behaviors, and
decision-making processes. Our results highlight the promise of cognitive
biases as potent indicators of user engagement, underscoring their significance
in designing effective digital content across fields like education and
marketing.