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Organization Mining Using Online Social Networks

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Abstract

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.

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Notes

  1. 1 http://www.facebook.com

  2. 2 http://www.linkedin.com

  3. 3 http://www.twitter.com

  4. 4 http://www.flickr.com

  5. 5 http://www.youtube.com

  6. 6 http://www.google.com

  7. 7 http://pipl.com

  8. 8 http://www.peekyou.com

  9. 9 Although, in theory, using a BFS crawler can return the target organization’s complete social network, in practice, a BFS crawler is not usable for crawling organizational social networks due to a BFS crawler’s low precision rates in identifying relevant profiles and due to many social networks providers’ limitations on the number of page requests (Twitter 2013). Moreover, due to BFS algorithm properties, there can be cases in which the BFS crawler collects employee profiles with several thousands of Facebook friends, and even though most of these are not employees of the targeted organization, the crawler will still need to collect the profile pages of each one of these friends before continuing its crawling process and moving to more relevant profiles. Nevertheless, in cases where the organization is relatively small with a few dozen employees, a BFS crawler may be sufficient to crawl the entire organization.

  10. 10 All the organizations’ graphs presented throughout this paper are embedded as Scalable Vector Graphics (SVG) images, which enable the reader to zoom in and view each node in each graph.

  11. 11 In contrast to the (Baker and Faulkner 1993) study in which, based on the employees’ titles and the company’s organization charts, the company’s employees were divided into three management categories (top executives, middle managers, or junior managers), we decided to divide each organization’s employees only into two dichotomous groups – managers and non-managers – where to the best of our judgment employees in the manager’s group held some type of management role in the organization, ranging from sales manager to the organization’s CEO.

  12. 12 In all six organizations, our methods discovered more than 17,000 employees. Therefore, to manually determine if each employee in each organization held a management role or not was impractical; consequently, we evaluated our algorithms on 4,650 manually classified employees’ profiles.

  13. 13 In this study, we considered position information to be partial if the description field in the employee’s Facebook profile was not empty.

  14. 14 The centrality measures were calculated by using the Networkx Python package (Hagberg et al. 2008).

  15. 15 During the precision at T-10, T-20, and T-50 calculations, we only took into account the employees that we succeeded to manually classify. For example, if among the top 10 employees in S2 that received the highest HITS measure, we could only manually classify 6 employees out of which 4 employees held management positions. Then S2’s precision at T-10 would be equal to \(\frac {4}{6}=0.66\)

  16. 16 The diameter of a graph G is defined as the maximum eccentricity, where the eccentricity of a node v is the maximum distance from v to all other nodes in G.

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Authors and Affiliations

  1. Telekom Innovation Laboratories and Department of Information Systems Engineering, Ben Gurion University of the Negev, Beer Sheva, PO Box 653, Israel

    Michael Fire & Rami Puzis

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  1. Michael Fire
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  2. Rami Puzis
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Correspondence to Michael Fire.

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Fire, M., Puzis, R. Organization Mining Using Online Social Networks. Netw Spat Econ 16, 545–578 (2016). https://doi.org/10.1007/s11067-015-9288-4

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