Information Sharing Behaviour On Social Media by Nazlena Mohamad Ali ( Author )
, Zaid Amin ( Author )
, Alan F.Smeaton ( Author )
Attention and awareness when receiving and sharing information on social media are essential. Users can effectively identify misinformation, so they are not affected by the latent misleading. Through the vast digital growing information in social media today, increasing behavioural problems stemming from our one-click habit of easy decisions are blatantly faster than bot machines propagating misinformation. This spread of misinformation can do serious damage to individuals and society in general. However, although many kinds of work explored traceability and situation prediction on spreading misinformation, more work is required to prevent and understand the issue of distraction on human attention. Questions arise on how such technological intervention can significantly handle the lack of users' visual abilities and gain accuracy when deciding to share information. The book explores key factors that trigger and influence users' attention in information sharing, highlights Tune-Attention Model on the attention-based design in information sharing and the application named Visual Selective Attention System (VSAS), which implements an attention-based design approach. The realization of engaging in attentive behaviour in sharing information on social media is expected to reduce the spread of misinformation. Furthermore, attentive behavioural factors are needed to form the basis of developing interactions in the design of future social media application systems, which can avoid the potential harm caused by sharing misinformation. This book contributes to the knowledge of information-sharing behaviour to the public. This book also contributes to the discussion of opportunities and challenges in information sharing and user attention in helping to reduce the problems of misinformation sharing. Several guidelines for stakeholders and policymakers related to social media are also highlighted.
Penerbit UKM
2023-06-06
978-967-251-991-1
Computers & Technology
eBook
epub
File Size 1,899.58 KB
English
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