Discovering Leadership Roles in Email Workgroups

Email is a key communication tool for collaborative workgroups. In this paper, we investigate how team leadership roles can be inferred from a collection of email messages exchanged among team members. This task can be useful to monitor group leader’s performance, as well as to study other aspects of work group dynamics. Using a large email collection with several workgroups whose leaders were previously defined, we demonstrate that leadership positions can be predicted by a combination of traffic-based and textbased email patterns. Traffic-based patterns consist of information patterns that can be extracted from the message headers, such as frequency counts, message thread position and whether the message was broadcast to the entire workgroup or not. Textual patterns are represented by the message’s “email speech acts”,i.e., semantic information with the sender’s intent that can be automatically inferred by language usage. Using off-the-shelf learning algorithms, we obtained 96% accuracy and 88.2% in F-measure in predicting the leadership roles on 34 email-centered work groups.