Mining e-mail content for author identification forensics
Open accessAbstract
We describe an investigation into e-mail content mining for author identification, or authorship attribution, for the purpose of forensic investigation. We focus our discussion on the ability to discriminate between authors for the case of both aggregated e-mail topics as well as across different e-mail topics. An extended set of e-mail document features including structural characteristics and linguistic patterns were derived and, together with a Support Vector Machine learning algorithm, were used for mining the e-mail content. Experiments using a number of e-mail documents generated by different authors on a set of topics gave promising results for both aggregated and multi-topic author categorisation.
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Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138 · see citing works
Introduction to Modern Information Retrieval
Salton, G., & McGill, M. J. (1983) · Virtual Defense Library (Ministerio de Defensa) · Cited by 9,743 · see citing works
Text categorization with Support Vector Machines: Learning with many relevant features
Joachims, T. (1998) · Lecture notes in computer science · doi:10.1007/bfb0026683 · Cited by 8,134 · see citing works
Witten, I. H., & Frank, E. (2002) · ACM SIGMOD Record · doi:10.1145/507338.507355 · Cited by 5,163 · see citing works
A re-examination of text categorization methods
Yang, Y., & Liu, X. (1999) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/312624.312647 · Cited by 2,670 · see citing works
An Evaluation of Statistical Approaches to Text Categorization
Yang, Y. (1999) · Information Retrieval · doi:10.1023/a:1009982220290 · Cited by 1,945 · see citing works
Support vector machines for spam categorization
Drucker, H., Wu, D., & Vapnik, V. N. (1999) · IEEE Transactions on Neural Networks · doi:10.1109/72.788645 · Cited by 1,476 · see citing works
A Bayesian Approach to Filtering Junk E-Mail
Sahami, M., Dumais, S., Heckerman, D. E., & Horvitz, E. (1998) · Cited by 1,212 · see citing works
Department of Computer Science
Buisman, H., Gomez-rodriguez, M., & Hassanpour, S. (1993) · Journal of Computational and Applied Mathematics · doi:10.1016/0377-0427(93)90083-n · Cited by 1,212 · see citing works
Inference and disputed authorship : The Federalist
Mosteller, F. C., & Wallace, D. K. (1964) · Medical Entomology and Zoology · Cited by 916 · see citing works
Feature selection, perception learning, and a usability case study for text categorization
Ng, H. T., Goh, W. B., & Low, K. L. (1997) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/258525.258537 · Cited by 450 · see citing works
How Variable May a Constant be? Measures of Lexical Richness in Perspective
Tweedie, F. J., & Baayen, R. H. (1998) · Computers and the Humanities · doi:10.1023/a:1001749303137 · Cited by 432 · see citing works
Learning Rules that Classify E-Mail
Cohen, W. W. (1996) · Cited by 406 · see citing works
Authorship Attribution with Support Vector Machines
Diederich, J., Kindermann, J., Leopold, E., & Paaß, G. (2003) · Applied Intelligence · doi:10.1023/a:1023824908771 · Cited by 363 · see citing works
The State of Authorship Attribution Studies: Some Problems and Solutions
Rudman, J. (1997) · Computers and the Humanities · doi:10.1023/a:1001018624850 · Cited by 300 · see citing works
The Federalist Revisited: New Directions in Authorship Attribution
Holmes, D. I. (1995) · Literary and Linguistic Computing · doi:10.1093/llc/10.2.111 · Cited by 269 · see citing works
Neural network applications in stylometry: The Federalist Papers
Tweedie, F. J., Singh, S. K., & Holmes, D. I. (1996) · Computers and the Humanities · doi:10.1007/bf00054024 · Cited by 209 · see citing works
Predicting gender from electronic discourse
Thomson, R., & Murachver, T. (2001) · British Journal of Social Psychology · doi:10.1348/014466601164812 · Cited by 179 · cited 3× · see citing works
“More recently, in the context of e-mail authorship characterisation, Thomson et al [34] have investigated the existence of gender-preferential language styles in e-mail communication.”
BackgroundInfluentialEmpirical evaluations of language-based author identification techniques
Chaski, C. E. (2001) · International Journal of Speech Language and the Law · doi:10.1558/sll.2001.8.1.1 · Cited by 177 · see citing works
Authorship analysis: identifying the author of a program
Krsul, I., & Spafford, E. H. (1997) · Computers & Security · doi:10.1016/s0167-4048(97)00005-9 · Cited by 164 · see citing works
Did Shakespeare write a newly-discovered poem?
Thisted, R. A., & EFRON, B. (1987) · Biometrika · doi:10.1093/biomet/74.3.445 · Cited by 152 · see citing works
Software forensics: Can we track code to its authors?
Spafford, E. H., & Weeber, S. A. (1993) · Computers & Security · doi:10.1016/0167-4048(93)90055-a · Cited by 116 · see citing works
Author Unknown: On the Trail of Anonymous
Foster, D. W. (2000) · H. Holt eBooks · Cited by 110 · see citing works
Did Shakespeare Write a Newly-Discovered Poem?
Thisted, R. A., & Efron, B. (1987) · Biometrika · doi:10.2307/2336684 · Cited by 105 · see citing works
Software Forensics: Extending Authorship Analysis Techniques to Computer Programs
MacDonell, S. G., Buckingham, D. W., Gray, A. R., & Sallis, P. J. (2002) · Tuwhera (Auckland University of Technology) · Cited by 97 · see citing works
Shakespeare vs. fletcher: A stylometric analysis by radial basis functions
Lowe, D. G., & Matthews, R. (1995) · Computers and the Humanities · doi:10.1007/bf01829876 · Cited by 66 · see citing works
Separating Hyperplanes and the Authorship of the Disputed Federalist Papers
Bosch, R. A., & Smith, J. A. (1998) · American Mathematical Monthly · doi:10.1080/00029890.1998.12004933 · Cited by 65 · see citing works
Programming style authorship analysis
Oman, P., & Cook, C. (1989) · doi:10.1145/75427.75469 · Cited by 55 · see citing works
Analysing for authorship : a guide to the cusum technique
Farringdon, J., Morton, A. Q., Farringdon, M. G., et al. (1996) · University of Wales Press eBooks · Cited by 49 · see citing works
Computational stylistics using artificial neural networks
Waugh, S. (2000) · Literary and Linguistic Computing · doi:10.1093/llc/15.2.187 · Cited by 45 · see citing works
Multi-Topic E-mail Authorship Attribution Forensics
Vel, O. D., Anderson, A., Corney, M., & Mohay, G. (2001) · QUT ePrints (Queensland University of Technology) · Cited by 42 · see citing works
Self-citationIdentifying the authors of suspect email
Corney, M., Anderson, A., Mohay, G., & Vel, O. D. (2001) · QUT ePrints (Queensland University of Technology) · Cited by 34 · see citing works
Self-citationKernel-based text categorisation
Jalam, R., & Teytaud, O. (2002) · 2022 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2001.938452 · Cited by 29 · see citing works
IDENTIFIED: software authorship analysis with case-based reasoning
Sallis, P. J., MacDonell, S., MacLennan, G., et al. (1998) · Tuwhera (Auckland University of Technology) · Cited by 17 · see citing works
Flagged first, then by citations
Research works printed but not linked: 10
- [3]3. C. Apte, F. Damerau, and S. Weiss. "Text mining with decision rules and decision trees". In Workshop on Learning from text and the Web, Conference on Automated Learning and Discovery, 1998.
- [8]8. C. Crain. "The Bard's fingerprints". Lingua Franca, pages 29-39, 1998.
- [9]9. O. de Vel. "Evaluation of Text Document Categorisation Techniques for Computer Forensics". Journal of Computer Security, 1999. (Submitted).
- [10]10. O. de Vel. "Mining e-mail authorship". In Proc. Workshop on Text Mining, ACM International Conference on Knowledge Discovery and Data Mining (KDD'2000), 2000.
- [13]13. W. Elliot and R. Valenza. "Was the Earl of Oxford the true Shakespeare?". Notes and Queries, 38:501-506, 1991.
- [19]19. D. Khmelev. "Disputed authorship resolution using relative entropy for Markov chain of letters in a text".
- [20]20. In R. Baayen, editor, Proc. 4th Conference Int. Quantitative Linguistics Association, Prague, 2000.
- [24]24. T. Mitchell. Machine Learning. McGraw-Hill, New York, 1997.
- [33]33. O. Teytaud and R. Jalam. "Kernel-based text categorization". In International Joint Conference on Neural Networks (IJCNN'2001), 2001. Washington DC, to appear.
- [41]41. I. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco, 2000.
Cited by
Sorted by citations
Koroniotis, N., Moustafa, N., Sitnikova, E., & Turnbull, B. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.05.041 · Cited by 1,983 · see citing works
Zheng, R., Li, J., Chen, H., & Huang, Z. (2006) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.20316 · Cited by 519 · see citing works
Computational methods in authorship attribution
Koppel, M., Schler, J., & Argamon, S. (2009) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.20961 · Cited by 528 · see citing works
Understanding Plagiarism Linguistic Patterns, Textual Features, and Detection Methods
Alzahrani, S. M., Salim, N., & Abraham, A. (2012) · IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2011.2134847 · Cited by 299 · see citing works
On the Feasibility of Internet-Scale Author Identification
Narayanan, A., Paskov, H., Gong, N. Z., Bethencourt, J., Stefanov, E., Shin, E. C. R., & Song, D. (2012) · 2012 IEEE Symposium on Security and Privacy · doi:10.1109/sp.2012.46 · Cited by 261 · see citing works
Authorship Attribution for Social Media Forensics
Rocha, A., Scheirer, W. J., Forstall, C. W., Cavalcante, T., Theophilo, A., Shen, B., Carvalho, A. R. B., & Stamatatos, E. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2016.2603960 · Cited by 222 · see citing works
Continuous user authentication using multi-modal biometrics
Saevanee, H., Clarke, N., Furnell, S., & Biscione, V. (2015) · Computers & Security · doi:10.1016/j.cose.2015.06.001 · Cited by 109 · see citing works
Person Identification from Text and Speech Genre Samples
Goldstein-Stewart, J., Winder, R., & Sabin, R. E. (2009) · Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics on - EACL '09 · doi:10.3115/1609067.1609104 · Cited by 22 · see citing works
- Authors
- de Vel, O., Anderson, A., Corney, M., & Mohay, G.
- Affiliations
- Defence Science and Technology Organisation, Salisbury, Australia
- Queensland University of Technology (AU)
- ORCID
- Anderson, A.
- Venue
- ACM SIGMOD Record
- Published
- 2001-12
- Volume
- 30
- Issue
- 4
- Pages
- 55-64
- ISSN
- 0163-5808 (ISSN-L)
- OpenAlex
- W1975653240
- Cited by
- 487
- References
- 35
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 7 · 2023: 5 · 2022: 8 · 2021: 14 · 2020: 21 · 2019: 17 · 2018: 20 · 2017: 28 · 2016: 46 · 2015: 42 · 2014: 41 · 2013: 26 · 2012: 34
- FWCI
- 7.45
- Type
- journal article
- Language
- en
- Open access
- green
- www.sigmod.org
- Locations
- ACM SIGMOD Record (publishedVersion)
- QUT ePrints (Queensland University of Technology) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Physical Sciences › Computer Science › Information Systems › User Authentication and Security Systems
- Keywords
- authorship attribution, digital forensics, document features, linguistic patterns, structural properties, support vector machine
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