predicting leadership roles in email workgroups vitor r. carvalho, wen wu and william w. cohen...
DESCRIPTION
Overview: Corpus Study Study: How well we can predict leadership Textual and “network” features What are the most predictive features Large and special collection of s CSPACE corpus (aka GSIA corpus) Leaders (presidents of workgroups) were previously determinedTRANSCRIPT
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Predicting Leadership Roles in Email Workgroups
Vitor R. Carvalho, Wen Wu and William W. CohenCarnegie Mellon University
CEAS-2007, Aug 2nd 2007
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Motivation
Link to leadership studies in hci Importance of email Leadership at a distance,
communication media, etc.[Butler et al, 2002; Fussell et al, 2001]
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Overview: Corpus Study
Study: How well we can predict leadership
Textual and “network” features What are the most predictive features
Large and special collection of emails CSPACE corpus (aka GSIA corpus) Leaders (presidents of workgroups) were
previously determined
![Page 4: Predicting Leadership Roles in Email Workgroups Vitor R. Carvalho, Wen Wu and William W. Cohen Carnegie Mellon University CEAS-2007, Aug 2 nd 2007](https://reader034.vdocuments.site/reader034/viewer/2022051303/5a4d1b657f8b9ab0599b0111/html5/thumbnails/4.jpg)
CSPACE corpus 15,000 messages from 277 students Emails associated with a semester-long project (14
weeks) of Carnegie Mellon MBA students To simulate companies competing for market share
and profit, students were divided in 50 companies/teams (4 to 6 students/team)
Very real: student grades largely based (70%) on company financial performance and external board review
Most communication happened inside group Very rich in task negotiation. Presidents assigned in the beginning of the game. Presidents selected other team members through a
round-robin draft.
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Email dataset: network
= President…
Team A Team B
Team C Team D
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Evidence from email header: network features
Broadcast messages Sent to all other team
members
Non-Broadcast messages Not sent all team
members
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Evidence from email header: network features
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Textual Features: Email Acts
Cohen et al., EMNLP-04: classification of email content in terms of having “email speech acts”
Examples: Deliver, Request, Commit, Propose, Meeting, etc.
Ciranda: Java toolkit available online
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Textual Features: Email Acts
•We also used the associated ranking features, i.e., first (_1), second (_2), last (_last) and one but last (_butlast)•96 features total
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Experiments 1
10-fold cross-validation using SVM with linear kernel
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Experiments 2:
Restricting to a single president per group, using All Features: 96% of accuracy F1-measure of 0.882. It correctly predicts the president in 30 out of
34 groups (minimum of 20 messages)
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Analysis: Feature Selection with 2 test
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Analysis: Feature Selection with 2 test
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Analysis Overall, results are interesting
Suggests some types of evidence are correlated with leadership
How “natural” is this dataset? Semester long, 14 weeks No language restriction (flames, arguments, cheering, arrangements,
gossip, etc.) Grade depended on the team financial performance
Leadership & the choice of leader Laboratory setting of work mitigates the reliability of the
conclusions How much self-selection is going on in choosing "President" of
company? Are students behaving according to their expectations of how executives behave?
Presidents were selected on a popular vote: the class as a whole (277 students) elected the 50 presidents.
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Related Work
Community structure and leadership roles in email archives of organizations.
[Leuski, 2004][Tyler et al, 2003.]
Very different datasets, methods, validations, etc.
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Thank you.