A computational framework for social-media-based business analytics and knowledge creation: empirical studies of CyTraSS

ABSTRACT

Social media (SM) platforms greatly facilitate business information sharing, customer relationship building, and client emotion expression. However, managing knowledge acquired from SM messages is challenged by limited human cognitive capability. This paper describes a computational framework for developing intelligent SM-based business analytics and visualization. The research developed a proof-of-concept system named CyTraSS to support intelligent analyses and visualization of 2,318,691 messages posted by 740,070 users who discuss trafficking topics on Twitter. The results demonstrate theoretical insights and practical usability of the framework, enhance understanding of knowledge creation with SM technology, and provide novel findings for business managers and policy makers.

KEYWORDS:

• Business analytics
• Business intelligence
• Emotion analysis
• knowledge management
• social media analytics
• social network analysis

Acknowledgments

The first author was supported in part by grants and donations from the Jay Kneedler Distinguished Professorship in CIS, the WCU-HKU Academic Partnership, and the UCF In-House Research Program. Any opinions, findings, and conclusions expressed in this paper are those of the authors and do not necessarily reflect the views of the funding agencies. The authors thank the project members for their assistance and the journal editors and reviewers for their valuable comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the The Jay Kneedler Distinguished Professorship in Computer Information Systems [611077]; WCU-HKU Academic Partnership; UCF In-House Research Program [1060766].