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

TL;DR

This paper combines cognitive psychology and NLP to predict user engagement with synonyms on digital platforms, revealing that cognitive biases significantly influence decision-making and content interaction.

Contribution

It introduces the READ model integrating four cognitive biases to predict user engagement with synonyms, validated through a user survey.

Findings

Synonyms representing core ideas increase engagement

Ease-of-use and emotional impact boost user interaction

Cognitive biases are effective indicators of engagement

Abstract

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.