# Organization Mining Using Online Social Networks

arXiv: https://arxiv.org/abs/1303.3741 · HTML: https://mlsanity.com/paper/1303.3741/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2013.

## Built on by (most cited first, 3 of 3)

- [Disentangling homophily, community structure and triadic closure in networks](https://mlsanity.com/paper/2101.02510/) (2021)
- [Systematic assessment of the quality of fit of the stochastic block model for empirical networks](https://mlsanity.com/paper/2201.01658/) (2022)
- [Large-scale multi-objective influence maximisation with network downscaling](https://mlsanity.com/paper/2204.06250/) (2022)

## Abstract (arXiv)

Mature social networking services are one of the greatest assets of today's organizations. This valuable asset, however, can also be a threat to an organization's confidentiality. Members of social networking websites expose not only their personal information, but also details about the organizations for which they work. 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 a given target organization. Our results, obtained using centrality analysis and Machine Learning techniques applied to the structure of the informal relationships network, show that it is possible to identify leadership roles within the organization solely by this means. It is also possible to gain valuable non-trivial insights on an organization's structure by clustering its social network and gathering publicly available information on the employees within each cluster. Organizations wanting to conceal their internal structure, identity of leaders, location and specialization of branches offices, etc., must enforce strict policies to control the use of social media by their employees.