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Social network analysis for email classification

Published: 28 March 2008 Publication History

Abstract

The availability of a large corpus of emails in organizations, such as the Enron dataset (used in this work), is the motivation for this work. The attempt is to see if one can predict the organizational structure of Enron by using data mining algorithms and methodologies on this email dataset. The primary approach in this attempt is the analysis of email flows within the organization. Our results show that significant information about an organization's structure can be obtained even if the body (content) of emails is neglected. Enough relevant data is extracted about the 'email' social network using simple email flow analysis and associated statistics gaining an over all picture of the organizational structure. The longer term objective of this work is to show that readily available information can be used to determine relevant metrics by which one can reconstruct and verify the approximate social hierarchies within an organization or company.

References

[1]
J. Diesner and K. M. Carley, "Exploration of Communication Networks from the Enron Email Corpus" Carnegie Mellon University.
[2]
B. Klimt and Y. Yang, The Enron Corpus: A New Dataset for Email Classification Research, ECML 2004.
[3]
W. W. Cohen, CALD, CMU. Retrieved October 5, 2004, from http://www-2.cs.cmu.edu/~enron/
[4]
J. Shetty, and J. Adibi, The Enron Dataset Database Schema and Brief Statistical Report. Retr. Nov. 4 2004, http://www.isi.edu/~adibi/Enron/Enron_Dataset_Rep.
[5]
http://www.jstor.org/view/00359254/di993342/99p04867/0?frame=noframe&[email protected]/01cce44061005014569b&dpi=3&config=jstor
[6]
S. Martin, A. Sewani, B. Nelson, K. Chen, A. D. Joseph. Analyzing Behavioral Features for Email Classification, Proceedings of the IEEE Second Conference on Email and Anti-Spam (CEAS 2005), July, 2005.
[7]
L. Yu*, K. R. Al-asmari, and S. Ramaswamy. The Dynamics of Open-Source Project Developer Network, article submitted for publication.
[8]
R. Popping, (2000). Computer-assisted Text Analysis. Thousand Oaks, CA: Sage Publications.
[9]
http://home.dei.polimi.it/matteucc/Clustering/tutorial_html/hierarchical.html
[10]
http://www.resample.com/xlminer/help/HClst/HClst_intro.htm

Cited By

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  • (2022)Social Semantic Web MiningundefinedOnline publication date: 9-Apr-2022
  • (2019)Using Conformal Prediction for Multi-label Document Classification in e-Mail Support SystemsAdvances and Trends in Artificial Intelligence. From Theory to Practice10.1007/978-3-030-22999-3_28(308-322)Online publication date: 9-Jul-2019
  • (2017)An email attachment is worth a thousand words, or is it?Proceedings of the 1st International Conference on Internet of Things and Machine Learning10.1145/3109761.3109765(1-10)Online publication date: 17-Oct-2017
  • Show More Cited By

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cover image ACM Other conferences
ACMSE '08: Proceedings of the 46th annual ACM Southeast Conference
March 2008
548 pages
ISBN:9781605581057
DOI:10.1145/1593105
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 March 2008

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Author Tags

  1. business organization structures
  2. data mining
  3. mining e-mail archives

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  • Research-article

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ACM SE08
ACM SE08: ACM Southeast Regional Conference
March 28 - 29, 2008
Alabama, Auburn

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Overall Acceptance Rate 502 of 1,023 submissions, 49%

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Cited By

View all
  • (2022)Social Semantic Web MiningundefinedOnline publication date: 9-Apr-2022
  • (2019)Using Conformal Prediction for Multi-label Document Classification in e-Mail Support SystemsAdvances and Trends in Artificial Intelligence. From Theory to Practice10.1007/978-3-030-22999-3_28(308-322)Online publication date: 9-Jul-2019
  • (2017)An email attachment is worth a thousand words, or is it?Proceedings of the 1st International Conference on Internet of Things and Machine Learning10.1145/3109761.3109765(1-10)Online publication date: 17-Oct-2017
  • (2017)Email Classification Research Trends: Review and Open IssuesIEEE Access10.1109/ACCESS.2017.27021875(9044-9064)Online publication date: 2017
  • (2014)Identifying a Criminal's Network of TrustProceedings of the 2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems10.1109/SITIS.2014.64(309-316)Online publication date: 23-Nov-2014
  • (2013)Leveraging social network analysis with topic models and the Semantic Web extendedWeb Intelligence and Agent Systems10.5555/2590097.259009911:4(303-314)Online publication date: 1-Oct-2013
  • (2012)Social feature-based enterprise email classification without examining email contentsJournal of Network and Computer Applications10.1016/j.jnca.2011.11.01035:2(770-777)Online publication date: 1-Mar-2012
  • (2011)Enterprise Email Classification Based on Social Network FeaturesProceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining10.1109/ASONAM.2011.89(532-536)Online publication date: 25-Jul-2011
  • (2010)Enhancing social network analysis with a concept-based text mining approach to discover key members on a virtual community of practiceProceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part II10.5555/1885375.1885442(591-600)Online publication date: 8-Sep-2010
  • (2010)Topic-based social network analysis for virtual communities of interests in the Dark WebACM SIGKDD Workshop on Intelligence and Security Informatics10.1145/1938606.1938615(1-9)Online publication date: 25-Jul-2010
  • Show More Cited By

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