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This study introduces and empirically tests a novel predictive model fordigital information engagement (IE) - the READ model, an acronym for the fourpivotal attributes of engaging information: Representativeness, Ease-of-use,Affect, and Distribution. Conceptualized within the theoretical framework ofCumulative Prospect Theory, the model integrates key cognitive biases withcomputational linguistics and natural language processing to develop amultidimensional perspective on information engagement. A rigorous testingprotocol was implemented, involving 50 randomly selected pairs of synonymouswords (100 words in total) from the WordNet database. These words' engagementlevels were evaluated through a large-scale online survey (n = 80,500) toderive empirical IE metrics. The READ attributes for each word were thencomputed and their predictive efficacy examined. The findings affirm the READmodel's robustness, accurately predicting a word's IE level and distinguishingthe more engaging word from a pair of synonyms with an 84% accuracy rate. TheREAD model's potential extends across various domains, including business,education, government, and healthcare, where it could enhance contentengagement and inform AI language model development and generative text work.Future research should address the model's scalability and adaptability acrossdifferent domains and languages, thereby broadening its applicability andefficacy.

A Predictive Model of Digital Information Engagement: Forecasting User  Engagement With English Words by Incorporating Cognitive Biases,  Computational Linguistics and Natural Language Processing