Can Algorithms Efficiently Identify Interpretable and Persuasive Message Features? An Agnostic Causal Machine Learning Approach

= 819). This agnostic approach discovered that messages emphasizing negative health consequences increased argument strength and message persuasiveness. In contrast, politicizing cues reduced the message persuasiveness of social media messages promoting COVID-19 vaccines. We discussed the strengths and limitations of this approach for future message effects research.

Authors

Luhang Sun (ORCID: https://orcid.org/0000-0002-3502-2440)
Sijia Yang (ORCID: https://orcid.org/0000-0003-4209-9881)
Y Wang
Yoo Ji Suh
Zening Duan
Ran Tao
Yibing Sun
Jiaying Liu

Institutions

Fairfield University (US)
University of Wisconsin–Madison (US)
University of California, Santa Barbara (US)
National University of Singapore (SG)
The University of Texas at Austin (US)

Publication Details

Journal: Health Communication
Published: 2026-04-20
DOI: https://doi.org/10.1080/10410236.2026.2661405
Primary Topic: Behavioral Health and Interventions
Type: article
Field-Weighted Citation Impact: 0.00

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article

Can Algorithms Efficiently Identify Interpretable and Persuasive Message Features? An Agnostic Causal Machine Learning Approach

Luhang Sun, Sijia Yang, Y Wang, Yoo Ji Suh et al.

Apr 20, 2026

Health Communication

Behavioral Health and Interventions

article

Can Algorithms Efficiently Identify Interpretable and Persuasive Message Features? An Agnostic Causal Machine Learning Approach

Luhang Sun, Sijia Yang, Y Wang, Yoo Ji Suh, Zening Duan, Ran Tao, Yibing Sun, Jiaying Liu

April 20, 2026article en

Abstract

= 819). This agnostic approach discovered that messages emphasizing negative health consequences increased argument strength and message persuasiveness. In contrast, politicizing cues reduced the message persuasiveness of social media messages promoting COVID-19 vaccines. We discussed the strengths and limitations of this approach for future message effects research.

Health Communication

Fairfield University (US), University of Wisconsin–Madison (US), University of California, Santa Barbara (US), National University of Singapore (SG), The University of Texas at Austin (US)

Good health and well-being

Openalex Percentile: Top 10%

Behavioral Health and Interventions

DOI Source OpenAlex

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