Abstract
Authorship verification is the task of determining whether a specific individual did or did not write a text, which very naturally can be reduced to the binary-classification problem. This paper deals with the authorship verification of short email messages. Hereafter, we use “message” to identify the content of the information that is transmitted by email. The proposed method implements the binary classification with a sequence-to-sequence (seq2seq) model and trains a convolutional neural network (CNN) on positive (written by the “target” user) and negative (written by “someone else”) examples. The proposed method differs from previously published works, which represent text by numerous stylometric features, by requiring neither advanced text preprocessing nor explicit feature extraction. All messages are submitted to the CNN “as is,” after padding to the maximal length and replacing all words by their ID numbers. CNN learns the most appropriate features with backpropagation and then performs classification. The experiments performed on the Enron dataset using the TensorFlow framework show that the CNN classifier verifies message authorship very accurately.
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Notes
- 1.
- 2.
We kept the default settings of the CNN model in the TensorFlow framework, which are as follows: number of embedding dimensions is 128; filter sizes are 3, 4, and 5; number of filters is 128, dropout probability is 0.5, L2 regularization lambda is 0, batch size is 64.
- 3.
The best accuracy of \(89\%\) for 40 users from the Enron dataset was reported in [5].
- 4.
We ran our model with 500 epochs.
- 5.
Obtained from training on one of the users.
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Acknowledgments
The author is grateful to Vlad Vavilin and Mark Mishaev for the implementation and running the experiments using the TensorFlow framework.
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Litvak, M. (2019). Deep Dive into Authorship Verification of Email Messages with Convolutional Neural Network. In: Lossio-Ventura, J., Muñante, D., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig 2018. Communications in Computer and Information Science, vol 898. Springer, Cham. https://doi.org/10.1007/978-3-030-11680-4_14
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