It is easy to hide the true identity of the author of an email. The author's actual name, email address, etc. can be changed arbitrarily to deceive an email receiver. For example, a sender can change his/her identity in the email header to send different emails to various recipients. Therefore, in this paper, we investigate techniques for authorship similarity detection from the text content of a short length, topic-free email. 150 stylistic cues are identified for this problem. A frequent pattern and machine learning based method is proposed. Extensive experiment results are also presented for the Enron email data set. © 2011 Springer-Verlag.
CITATION STYLE
Chen, X., Hao, P., Chandramouli, R., & Subbalakshmi, K. P. (2011). Authorship similarity detection from email messages. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6871 LNAI, pp. 375–386). https://doi.org/10.1007/978-3-642-23199-5_28
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