  Health misinformation on social media devastates physical and mental health,
invalidates health gains, and potentially costs lives. Understanding how health
misinformation is transmitted is an urgent goal for researchers, social media
platforms, health sectors, and policymakers to mitigate those ramifications.
Deep learning methods have been deployed to predict the spread of
misinformation. While achieving the state-of-the-art predictive performance,
deep learning methods lack the interpretability due to their blackbox nature.
To remedy this gap, this study proposes a novel interpretable deep learning
approach, Generative Adversarial Network based Piecewise Wide and Attention
Deep Learning (GAN-PiWAD), to predict health misinformation transmission in
social media. Improving upon state-of-the-art interpretable methods, GAN-PiWAD
captures the interactions among multi-modal data, offers unbiased estimation of
the total effect of each feature, and models the dynamic total effect of each
feature when its value varies. We select features according to social exchange
theory and evaluate GAN-PiWAD on 4,445 misinformation videos. The proposed
approach outperformed strong benchmarks. Interpretation of GAN-PiWAD indicates
video description, negative video content, and channel credibility are key
features that drive viral transmission of misinformation. This study
contributes to IS with a novel interpretable deep learning method that is
generalizable to understand other human decision factors. Our findings provide
direct implications for social media platforms and policymakers to design
proactive interventions to identify misinformation, control transmissions, and
manage infodemics.
