Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/79515

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

File Size Format  
0145.pdf 691.03 kB Adobe PDF View/Open

Item Summary

Title:An Interpretable Deep Learning Approach to Understand Health Misinformation Transmission on YouTube
Authors:Xie, Jiaheng
Chai, Yidong
Liu, Xiao
Keywords:Explainable Artificial Intelligence (XAI)
data mining
interpretable deep learning
misinformation
youtube
Date Issued:04 Jan 2022
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.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/79515
ISBN:978-0-9981331-5-7
DOI:10.24251/HICSS.2022.183
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Explainable Artificial Intelligence (XAI)


Please email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.

This item is licensed under a Creative Commons License Creative Commons