Expertise identification using email communications

By Christopher S. Campbell, Paul P. Maglio, Alex Cozzi, Byron Dom. International Conference on Information and Knowledge Management, 2003

408 citations recorded by the source corpus.

Subfields: Computer Science Applications, Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/3a5c6e31172b00876551eb56cad57411756433e8

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In Computer Science Applications

In Information Systems

In Statistical and Nonlinear Physics

Citations in the corpus

Ambient Awareness and Knowledge Acquisition: Using Social Media to Learn "Who Knows What" and "Who Knows Whom" (2015, background): “The things a person says to someone else in routine communication—facts about work assignments, questions about problems, reports of items learned in meetings—provide explicit cues about what that person knows (Campbell et al. 2003).”
Enhancing Expert Finding Using Organizational Hierarchies (2009, methodology): “In order to overcome this problem, many systems [4],[5],[30],[31] use graph-based ranking algorithms, including HITS [14] and PageRank [20], in addition to content analysis, to locate experts.”
Learning to recognize reliable users and content in social media with coupled mutual reinforcement (2009, background): “[5] compute the score of HITS over the user-user graph in a network of e-mail exchanges, showing that it is more correlated to quality than other metrics.”
Expertise networks in online communities: structure and algorithms (2007, neutral): “[8] and Dom et al.”
Social Media, Knowledge Sharing, and Innovation: Toward a Theory of Communication Visibility (2014, positive): “…whether the content of messages exchanged between communication partners could contain sufficient information about work tasks for learning to take place, Campbell et al. (2003) devised an algorithm that predicted topical experts based on text mined from email messages in two large organizations.”
Finding high-quality content in social media (2008, methodology): “[8] computed the authority score of HITS over the user-user graph in a network of e-mail exchanges, showing that it is more correlated to quality than other simpler metrics.”
Formal models for expert finding in enterprise corpora (2006, background): “Or, they may require a highly trained specialist to consult about a very specific problem in a particular programming language, standard, law, etc. Identifying experts may reduce costs and facilitate a better solution than could be achieved otherwise.”
Voting for candidates: adapting data fusion techniques for an expert search task (2006, methodology): “• Emails sent or received by the candidate [3, 5, 8]”