ArticleComputers in human behavior2021
Machine learning techniques and older adults processing of online information and misinformation: A covid 19 study.
Article in Computers in human behavior, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.
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Who cites it
33 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Older adults and online health misinformation: a systematic literature review.BMC psychology · 2026Pooled it
- The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions.Computers in biology and medicine · 2022Pooled it
- Unpacking the theoretical role of fear of missing out in the relationship between social networking intensity and well-being among Indian college students.Discover mental health · 2025Article
- Deep learning model for diagnosing lupus erythematosus in cardiac patients using ECG and audio spectrograms.Scientific reports · 2025Article
- The internet usage increases fear of infection with Covid-19.Scientific reports · 2025Article
- Article
- Leveraging Chatbots to Combat Health Misinformation for Older Adults: Participatory Design Study.JMIR formative research · 2024Article
- Navigating persuasive strategies in online health misinformation: An interview study with older adults on misinformation management.PloS one · 2024Article
- Infodemic: Challenges and solutions in topic discovery and data process.Archives of public health = Archives belges de sante publique · 2023Article
- Deepfakes and scientific knowledge dissemination.Scientific reports · 2023Article
- Article
- A scoping review of digital health interventions for combating COVID-19 misinformation and disinformation.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Changes in Internet use patterns among older adults in England from before to after the outbreak of the COVID-19 pandemic.Scientific reports · 2023Article
- Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives.Array (New York, N.Y.) · 2023Review
- Social media-based COVID-19 sentiment classification model using Bi-LSTM.Expert systems with applications · 2023Article
- Information Cocoons on Short Video Platforms and Its Influence on Depression Among the Elderly: A Moderated Mediation Model.Psychology research and behavior management · 2023Article
- Detecting Tweets Containing Cannabidiol-Related COVID-19 Misinformation Using Transformer Language Models and Warning Letters From Food and Drug Administration: Content Analysis and Identification.JMIR infodemiology · 2023Article
- Article
- The effect of pecha-kucha training on fear and belief in myths of COVID-19 in elderly women.International journal of disaster risk reduction : IJDRR · 2022Article
- The psychological reassurance effect of mobile tracing apps in Covid-19 Era.Computers in human behavior · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
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