Research on Visual Narrative Strategies for Big Health Communication——Based on the Health Belief Model

Authors

  • Yuwei Chen Nanfang College Guangzhou Author
  • Tianyuan Shang The University of Melbourne Author
  • Xinbao Zhang Nanfang College Guangzhou Author

DOI:

https://doi.org/10.70695/IAAI202504A4

Keywords:

Health communication; Probability calibration; A/B experiment; Scalability

Abstract

In the current media environment, with distracted attention and information overload, health information is often difficult to turn into actual protective behavior. Based on HBM theory, a narrative mechanism of "construct-visual grammar" mapping and constraint solving was created, and a complete system of data collection, construct estimation , parametric visual design, WebGL/Canvas rendering, and A/B evaluation was established. Bootstrap and permutation tests were performed, and means, 95% confidence intervals, and effect sizes were reported, which were calibrated together with reliability curves and ECE assessments. Compared with static infographics and generic templates, HBM visualization significantly improved correct understanding, protection intention, click-through rate, and user dwell time. It also achieved the minimum ECE value and a high adoption area under the balanced narrative type. The system maintained scalability from 1080p to 4K and within the range of 30 to 60 seconds. The overlap between chronic disease, vaccination, and mental health scenarios was high. HBM-based visual narrative constructs an explainable, measurable, and easily deployable strategic framework for health communication. While complying with regulations and ensuring accessibility, it improves audience understanding and behavioral tendencies, while demonstrating a strong ability to migrate across domains.

Published

2025-12-31

How to Cite

Chen, Y., Shang, T., & Zhang, X. (2025). Research on Visual Narrative Strategies for Big Health Communication——Based on the Health Belief Model. Innovative Applications of AI, 2(4), 131-141. https://doi.org/10.70695/IAAI202504A4