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.
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