Phrasing for UX: Enhancing Information Engagement through Computational Linguistics and Creative Analytics

TL;DR

This paper introduces the READ model, a computational linguistics framework that predicts and enhances Information Engagement on digital platforms by analyzing textual features, validated through rigorous testing and practical text modifications.

Contribution

The study presents the novel READ model that quantifies linguistic predictors of engagement and demonstrates its effectiveness in improving user interaction through targeted text modifications.

Findings

READ model predicts engagement with high accuracy (up to 0.97)

Text modifications based on the model increase engagement metrics by over 10%

Linguistic factors significantly influence digital information engagement

Abstract

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.