Determining Appropriate Social Media Sites for Knowledge Sharing: A Data Science Approach

Determining Appropriate Social Media Sites for Knowledge Sharing: A Data Science Approach

Albert Gyamfi
Copyright: © 2021 |Pages: 27
DOI: 10.4018/978-1-7998-6792-0.ch002
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Abstract

The study aims at developing a fully developed and operational cloud-based published website for predicting appropriate social media for sharing knowledge during an outbreak. The media richness theory (MRT) is used in establishing the relationship between the richness of a social media platform, which is based on four criteria—ability to provide feedback, multiple cues, language variety, and personal focus—and the level of equivocality and uncertainty in the knowledge sharing task. A survey is used to gather data on the use of four social network sites (SNSs) (Facebook, Twitter, YouTube, and Instagram) that were mostly used for sharing knowledge during the COVID-19 pandemic. Data science techniques are used to analyze the data and develop a system for selecting appropriate social media for sharing knowledge.
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Problem Statement

Knowledge transferring and Knowledge sharing plays a pioneering role and have increasingly gained a pivotal position in organizations especially in the data science communities (Carvalho and Gomes, 2017). In order to succeed in the exponentially increasing competitive market, organizations are required to implement diverse strategies to acquire, maintain, store, and disseminate strategic knowledge to be able to reuse when the need arises. As a result, many organizations feel the urge to acquire and use different technologies to enhance efficiency in sharing and accessing knowledge in efficient manner (Barros et al., 2015) (Diamantini et al., 2013). Among the technologies widely chosen for knowledge sharing are social media websites. However, with the plethora of social media platforms with different functionalities and media characteristics, selecting the appropriate medium for a giving communication tasks becomes a challenge, especially in the course of a disaster or a pandemic.

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