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.

翻译：本研究提出并实证检验了一种面向数字信息参与度（IE）的新型预测模型——READ模型，该缩写代表信息参与度的四个关键属性：代表性（Representativeness）、易用性（Ease-of-use）、情感（Affect）和分布（Distribution）。基于累积前景理论的理论框架，该模型将关键认知偏差与计算语言学及自然语言处理相结合，构建了信息参与度的多维视角。研究实施了严格的测试方案，从WordNet数据库中随机选取50组同义词对（共100个词汇），通过大规模在线调查（n=80,500）评估这些词汇的参与度水平，从而得出经验性IE指标。随后计算每个词的READ属性并检验其预测效能。研究结果证实了READ模型的稳健性，该模型不仅能准确预测词汇的IE水平，还能以84%的准确率区分同义词对中更具吸引力的词汇。READ模型在商业、教育、政府和医疗保健等多个领域具有广泛的应用潜力，可增强内容参与度，并为人工智能语言模型开发及生成式文本工作提供参考。未来研究应关注模型在不同领域和语言中的可扩展性与适应性，以提升其适用范围和有效性。