In Computers in human behavior ; h5-index 125.0
This study is informed by two research gaps. One, Artificial Intelligence's (AI's) Machine Learning (ML) techniques have the potential to help separate information and misinformation, but this capability has yet to be empirically verified in the context of COVID-19. Two, while older adults can be particularly susceptible to the virus as well as its online infodemic, their information processing behaviour amid the pandemic has not been understood. Therefore, this study explores and understands how ML techniques (Study 1), and humans, particularly older adults (Study 2), process the online infodemic regarding COVID-19 prevention and cure. Study 1 employed ML techniques to classify information and misinformation. They achieved a classification accuracy of 86.7% with the Decision Tree classifier, and 86.67% with the Convolutional Neural Network model. Study 2 then investigated older adults' information processing behaviour during the COVID-19 infodemic period using some of the posts from Study 1. Twenty older adults were interviewed. They were found to be more willing to trust traditional media rather than new media. They were often left confused about the veracity of online content related to COVID-19 prevention and cure. Overall, the paper breaks new ground by highlighting how humans' information processing differs from how algorithms operate. It offers fresh insights into how during a pandemic, older adults-a vulnerable demographic segment-interact with online information and misinformation. On the methodological front, the paper represents an intersection of two very disparate paradigms-ML techniques and interview data analyzed using thematic analysis and concepts drawn from grounded theory to enrich the scholarly understanding of human interaction with cutting-edge technologies.
Choudrie Jyoti, Banerjee Snehasish, Kotecha Ketan, Walambe Rahee, Karende Hema, Ameta Juhi
AI, COVID-19 pandemic, Information-misinformation, Interview, Machine learning techniques, Older adult