Organization Mining Using Online Social Networks

Research output: Contribution to journalArticlepeer-review


Complementing the formal organizational structure of a business are the informal connections among employees. These relationships help identify knowledge hubs, working groups, and shortcuts through the organizational structure. They carry valuable information on how a company functions de facto. In the past, eliciting the informal social networks within an organization was challenging; today they are reflected by friendship relationships in online social networks. In this paper we analyze several commercial organizations by mining data which their employees have exposed on Facebook, LinkedIn, and other publicly available sources. Using a web crawler designed for this purpose, we extract a network of informal social relationships among employees of targeted organizations. Our results show that it is possible to identify leadership roles within the organization solely by using centrality analysis and machine learning techniques applied to the informal relationship network structure. Valuable non-trivial insights can also be gained by clustering an organization’s social network and gathering publicly available information on the employees within each cluster. Knowledge of the network of informal relationships may be a major asset or might be a significant threat to the underlying organization.

Original languageAmerican English
Pages (from-to)545-578
Number of pages34
JournalNetworks and Spatial Economics
Issue number2
StatePublished - 1 Jun 2016


  • Facebook
  • Leadership roles
  • LinkedIn
  • Machine learning
  • Organizational data mining
  • Organizational social network privacy
  • Social network data mining
  • Social network privacy

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications
  • Artificial Intelligence


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