ABSTRACT
We examine the behavioral patterns of email usage in a large-scale enterprise over a three-month period. In particular, we focus on two main questions: (Q1) what do replies depend on? and (Q2) what is the gain of augmenting contacts through the friends of friends from the email social graph? For Q1, we identify and evaluate the significance of several factors that affect the reply probability and the email response time. We find that all factors of our considered set are significant, provide their relative ordering, and identify the recipient list size, and the intensity of email communication between the correspondents as the dominant factors. We highlight various novel threshold behaviors and provide support for existing hypotheses such as that of the least-effort reply. For Q2, we find that the number of new contacts extracted from the friends-of-friends relationships amounts to a large number, but which is still a limited portion of the total enterprise size. We believe that our results provide significant insights towards informed design of advanced email features, including those of social-networking type.
- L. Adamic and E. Adar. How to search a social network. Social Networks, 27(3):187--203, 2005.Google ScholarCross Ref
- T. J. Allen and S. I. Cohen. Information Flow in Research and Development Laboratories. Administrative Science Quarterly, 14(1):12--19, 1969.Google ScholarCross Ref
- J.-Y. L. Boudec. Performance Evaluation Lecture Notes. http://ica1www.epfl.ch/perfeval/lectureNotes.htm.Google Scholar
- C. S. Campbell, P. P. Maglio, A. Cozzi, and B. Dom. Expertise Identification using Email Communication. In Proc. of CIKM 2003, pages 528--531, 2003. Google ScholarDigital Library
- K. J. Delaney and V. Vara. Will Social Features Make Email Sexy Again? The Wall Street Journal, Oct. 2007.Google Scholar
- B. Dom, I. Eiron, A. Cozzi, and Y. Zhang. Graph-based ranking algorithms for E-mail expertise analysis. In Proc. of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pages 42--48, San Diego, California, 2003. Google ScholarDigital Library
- J.-P. Eckmann, E. Moses, and D. Sergi. Entropy of dialogues creates coherent structures in e-mail traffic. In Proc. Natl. Acad. Sci. 101:14333--14337, 2004.Google ScholarCross Ref
- G. Kossinets, J. Kleinberg, and D. Watts. The structure of information pathways in a social communication network. In ACM SIGKDD, pages 435--443, New York, NY, USA, 2008. ACM. Google ScholarDigital Library
- G. Kossinets and D. Watts. Empirical analysis of an evolving social network. In Science, 311:88Ý U90, 2006.Google ScholarCross Ref
- C. Neustaedter, A. J. B. Brush, and M. A. Smith. Beyond From and Received: Exploring the Dynamics of Email Triage. In Proc. of ACM CHI, 1977--1980, 2005. Google ScholarDigital Library
- C. Neustaedter, A. J. B. Brush, M. A. Smith, and D. Fisher. The Social Network and Relationship Finder: Social Sorting for Email Triage. In Fifth Conference on Email and Anti-Spam, CEAS, 2005.Google Scholar
- M. F. Schwartz and D. C. M. Wood. Discovering Shared Interests Among People Using Graph Analysis of Global Electronic Mail Traffic. ACM Communications, 36(8):78--89, 1993. Google ScholarDigital Library
- X. Shi, L. Adamic, and M. Strauss. Network of Strong Ties. Pysica A, 378(1):33--47, 2007.Google Scholar
- L. Sproull and S. Kiesler. Reducing Social Context Cues: Electronic Email in Organizational Communications. Management Science, 32(11):1492--1512, 1986. Google ScholarDigital Library
- J. R. Tyler and J. C. Tang. When can i expect an email response? a study of rhythms in email usage. In Proc. of ECSCW, 239--258, 2003. Google ScholarDigital Library
- J. R. Tyler, D. M. Wilkinson, and B. A. Huberman. Email as spectroscopy: automated discovery of community structure within organization. Communities and technologies, pages 81--96, 2003. Google ScholarDigital Library
- G. D. Venoila, L. Dabbish, J. J. Cadiz, and A. Gupta. Supporting Email Workflow. Technical Report MSR-TR-2001-88, Microsoft Research, 2001.Google Scholar
- J. Zhang, M. S. Ackerman, and L. Adamic. Expertise Networks in Online Communities. In Proc. of WWW 2007, Banff, Alberta, Canada, 2007. Google ScholarDigital Library
Index Terms
- Behavioral profiles for advanced email features
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