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

Wingyan Chung, Elizabeth Mustaine, Daniel Zeng

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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.

Original languageEnglish (US)
JournalEnterprise Information Systems
DOIs
StateAccepted/In press - 2020

Keywords

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

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems and Management

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