An Interpretable Deep Learning Approach to Understand Health Misinformation Transmission on YouTube

dc.contributor.author Xie, Jiaheng
dc.contributor.author Chai, Yidong
dc.contributor.author Liu, Xiao
dc.date.accessioned 2021-12-24T17:30:02Z
dc.date.available 2021-12-24T17:30:02Z
dc.date.issued 2022-01-04
dc.description.abstract Health misinformation on social media devastates physical and mental health, invalidates health gains, and potentially costs lives. Deep learning methods have been deployed to predict the spread of misinformation, but they lack the interpretability due to their blackbox nature. To remedy this gap, this study proposes a novel interpretable deep learning, Generative Adversarial Network based Piecewise Wide and Attention Deep Learning (GAN-PiWAD), to predict health misinformation transmission in social media. 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. 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 that is generalizable to understand human decisions. We provide direct implications to design interventions to identify misinformation, control transmissions, and manage infodemics.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2022.183
dc.identifier.isbn 978-0-9981331-5-7
dc.identifier.uri http://hdl.handle.net/10125/79515
dc.language.iso eng
dc.relation.ispartof Proceedings of the 55th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Explainable Artificial Intelligence (XAI)
dc.subject data mining
dc.subject interpretable deep learning
dc.subject misinformation
dc.subject youtube
dc.title An Interpretable Deep Learning Approach to Understand Health Misinformation Transmission on YouTube
dc.type.dcmi text
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