用语言模型提升数字内容吸引力，让信息更易被选中、评价更高、留存更久。
Phrasing for UX: Enhancing Information Engagement through Computational Linguistics and Creative Analytics
- 提出READ模型，量化文本代表性、易用性、情感与分布对用户参与度的影响。
- 实验证明模型预测准确率最高达97%，修改文本后选择率、评分和留存率均提升11%。
- 适合教育、健康、媒体等领域优化内容表达，实用性强。
本研究探讨了文本特征与数字平台信息参与度（IE）之间的关系，强调计算语言学与分析对用户互动的影响。提出READ模型，用于量化代表性、易用性、情感和分布等关键预测因子，可有效预测参与度（准确率：0.94）、感知度（准确率：0.85）、坚持度（准确率：0.81）和总体信息参与度（准确率：0.97）。通过A/B测试与随机试验验证，基于模型优化文本可显著提升效果：提高代表性与积极情感，使选择率提升11%，评估平均分从3.98升至4.46，留存率提高11%。研究揭示语言因素在信息参与中的核心作用，为教育、健康、媒体等领域提供可落地的优化框架。
原文摘要 · Abstract (English)
This study explores the relationship between textual features and Information Engagement (IE) on digital platforms. It highlights the impact of computational linguistics and analytics on user interaction. The READ model is introduced to quantify key predictors like representativeness, ease of use, affect, and distribution, which forecast engagement levels. The model's effectiveness is validated through AB testing and randomized trials, showing strong predictive performance in participation (accuracy: 0.94), perception (accuracy: 0.85), perseverance (accuracy: 0.81), and overall IE (accuracy: 0.97). While participation metrics are strong, perception and perseverance show slightly lower recall and F1-scores, indicating some challenges. The study demonstrates that modifying text based on the READ model's insights leads to significant improvements. For example, increasing representativeness and positive affect boosts selection rates by 11 percent, raises evaluation averages from 3.98 to 4.46, and improves retention rates by 11 percent. These findings highlight the importance of linguistic factors in IE, providing a framework for enhancing digital text engagement. The research offers practical strategies applicable to fields like education, health, and media.
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