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

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dc.contributor.authorXie, Jiaheng
dc.contributor.authorChai, Yidong
dc.contributor.authorLiu, Xiao
dc.date.accessioned2021-12-24T17:30:02Z
dc.date.available2021-12-24T17:30:02Z
dc.date.issued2022-01-04
dc.description.abstractHealth 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.extent10 pages
dc.identifier.doi10.24251/HICSS.2022.183
dc.identifier.isbn978-0-9981331-5-7
dc.identifier.urihttp://hdl.handle.net/10125/79515
dc.language.isoeng
dc.relation.ispartofProceedings of the 55th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectExplainable Artificial Intelligence (XAI)
dc.subjectdata mining
dc.subjectinterpretable deep learning
dc.subjectmisinformation
dc.subjectyoutube
dc.titleAn Interpretable Deep Learning Approach to Understand Health Misinformation Transmission on YouTube
dc.type.dcmitext

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