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Attention-based design and selective exposure amid COVID-19 misinformation sharing

Amin, Zaid orcid logoORCID: 0000-0001-5223-3058, Ali, Nazlena Mohamad orcid logoORCID: 0000-0002-2267-8328 and Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389 (2021) Attention-based design and selective exposure amid COVID-19 misinformation sharing. In: 23rd International Conference on Human-Computer Interaction, 24-29 July 2021, Washington, DC, USA (Online). ISBN 978-3-030-78468-3

Abstract
One of the significant limitations in human behaviour when receiving online information is our lack of visual cognitive abilities, the ability to pay greater attention in a short time. The question arises about how we handle online messages, which contain and send people with the same associated interests as ourselves, regarding social influences and individual beliefs. This study aims to provide some insight into misinformation sharing. The availability of enormous amounts of COVID-19 information makes the selectivity of messages likely limited by the distortion of perceptions in the communicating environment. It is also in line with the fact that human attention is essentially limited and depends on the conditions and tasks at hand. To understand this phenomenon, we proposed a Tuning Attention Model (TAM). The model proposes tuning and intervene in a user’s attention behaviour by incorporating an attention-based design when users decide to share COVID-19 misinformation. In pilot study results, we found that attention behaviour negatively correlated with misinformation sharing behaviour. The results justify that when attention behaviour increased, misinformation sharing behaviour will decrease. We suggest an attention-based design approached on social media application’s that could intervene in user attention and avoid selective exposure caused by the spread of COVID-19 misinformation. The study expected to produce continuous knowledge leading to non-coercive handling of sharing COVID-19 misinformation behaviour and laying the basis for overcoming misinformation issues.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Attention, design; selective exposure; COVID-19; misinformation sharing; user interfaces
Subjects:Computer Science > Multimedia systems
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: Human-Computer Interaction. Design and User Experience Case Studies. HCII 2021. Lecture Notes in Computer Science 12764. Springer. ISBN 978-3-030-78468-3
Publisher:Springer
Official URL:https://doi.org/10.1007/978-3-030-78468-3_34
Copyright Information:© 2021 Springer
Funders:Science Foundation Ireland, University research grant UKM GPK-4IR-2020-019
ID Code:26081
Deposited On:03 Aug 2021 10:09 by Alan Smeaton . Last Modified 05 Jan 2022 13:40
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