2026-10-15T22:45:44Z https://data.mendeley.com/oai

oai:data.mendeley.com/z6w3p58j9h.1 2023-06-06T07:19:24Z

Dvir, Nim Replication Data for: Words That Stick Predicting Decision Making and Synonym Engagement Using Cognitive Biases and Computational Linguistics Mendeley Data 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 Computational Linguistics Natural Language Processing Content Analysis Cognitive Aspect of Human-Computer System Human-Computer Interaction User Experience Dataset doi:10.17632/z6w3p58j9h.1 oai:data.mendeley.com/z6w3p58j9h.1 Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 https://data.mendeley.com/datasets/z6w3p58j9h 2023-06-06T07:19:24Z