[…] As a leaky pipe for communication, Enterprise Social Media (ESM) create special opportunities for analyzing social relations and producing insights based on social analytics. The digital traces of communication can be processed with algorithms that can help employees make connections, and help managers understand the organization’s informal information economy. A study by Green, Contractor, and Yao (2006) showed how a social networking application with algorithms to make emergent associations between people and user-generated content spurred cross-boundary interactions and knowledge sharing in environmental engineering and hydrological science research. This increased collaboration occurred because once users learned that others were interested in similar topics to them individuals were more willing to work to overcome disciplinary differences and understand one another, even if they did not share a common store of domain knowledge. The use of digital communication traces that have leaked out of secure channels and are available for mining with machine learning algorithms can also have disadvantages for organizational action.. (from “Enterprise Social Media: Definition, History, and Prospects for the Study of Social Technologies in Organizations, Paul M. Leonardi et alii, 2013)
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