A Predictive Model of Digital Information Engagement: Forecasting User
  Engagement With English Words by Incorporating Cognitive Biases,
  Computational Linguistics and Natural Language Processing
Nimrod Dvir Elaine Friedman Suraj Commuri Fan yang Jennifer Romano
This study introduces and empirically tests a novel predictive model for
digital information engagement (IE) - the READ model, an acronym for the four
pivotal attributes of engaging information: Representativeness, Ease-of-use,
Affect, and Distribution. Conceptualized within the theoretical framework of
Cumulative Prospect Theory, the model integrates key cognitive biases with
computational linguistics and natural language processing to develop a
multidimensional perspective on information engagement. A rigorous testing
protocol was implemented, involving 50 randomly selected pairs of synonymous
words (100 words in total) from the WordNet database. These words' engagement
levels were evaluated through a large-scale online survey (n = 80,500) to
derive empirical IE metrics. The READ attributes for each word were then
computed and their predictive efficacy examined. The findings affirm the READ
model's robustness, accurately predicting a word's IE level and distinguishing
the more engaging word from a pair of synonyms with an 84% accuracy rate. The
READ model's potential extends across various domains, including business,
education, government, and healthcare, where it could enhance content
engagement and inform AI language model development and generative text work.
Future research should address the model's scalability and adaptability across
different domains and languages, thereby broadening its applicability and
efficacy.