Michael Fire

Senior Academic

Organization Mining Using Online Social Networks

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.

Publication language English
Pages 545-578
Journal Networks and Spatial Economics
Volume 16
Issue number 2
Publication status Published - 01.06.2016

Keywords

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

ASJC Scopus subject areas

Software
Computer Networks and Communications
Artificial Intelligence
Access to Document
10.1007/s11067-015-9288-4
Other files and links
Link to publication in Scopus