Social Network Analysis and Mining

, Volume 3, Issue 3, pp 611–633 | Cite as

An empirical analysis of microblogging behavior in the enterprise

Original Article

Abstract

Popular social networking sites have revolutionized the way people interact on the Web. Researchers have studied social networks from numerous perspectives, mostly focusing on publicly available online social networks and microblogging sites, where interactions are in general informal. Enterprises, however, have recently being adopting and utilizing microblogging services as part of their day to day operations. Within an enterprise, microblogging behavior is bounded by main business and work culture, work practices, and everyday problems. Thus, content is formal (although exchanged in an informal setting), less noisy than in online social networks, and emphasizes on the business perspective. The goal of this paper is to study the topological properties of a corporate microblogging service, its dynamics and characteristics, and the interplay between its social and topical components. Through an extensive analysis of enterprise microblogging data, we provide insights on the structural properties of the extracted network of directed messages sent between users of a corporate microblogging service, as well as the lexical and topical alignment of users. We study homophily, finding a substantial level of alignment with respect to the network structure and users activity, as well as latent topical similarity and link probability. Our analysis suggests that users with strong local topical alignment tend to participate in focused interactions, whereas users with disperse interests contribute to multiple discussions, broadening the diversity of participants. We compare our results to traditional, general purpose, online social networks and discuss the implications of our findings.

Keywords

Enterprise microblogging Social media analytics Homophily Assortativity Social semantic similarity Social latent similarity 

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Copyright information

© Springer-Verlag Wien 2013

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Ming Hsieh Department of Electrical EngineeringUniversity of Southern CaliforniaLos AngelesUSA

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