Replication Data for: Words That Stick Predicting Decision Making and Synonym Engagement Using Cognitive Biases and Computational Linguistics

This research utilizes cognitive neuroscience and information systems research to predict user engagement and decision-making in digital platforms. By applying Natural Language Processing (NLP) techniques and cognitive bias theories, we investigate user interactions with synonyms in digital content. Our approach incorporates four cognitive biases - representativeness, ease-of-use (processing fluency), affect-biased attention, and distribution/availability (R.E.A.D) - into a comprehensive model. The model's predictive capacity was evaluated using a large user survey, revealing that synonyms representative of core concepts, easy to process, emotionally resonant, and readily available, fostered increased user engagement. Importantly, our research provides a novel perspective on human-computer interaction, digital habits, and decision-making processes. Findings underscore the potential of cognitive biases as powerful predictors of user engagement, emphasizing their role in effective digital content design across education, marketing, and beyond.