Abstract:
Post-authorship attribution is a scientific process of using stylometric features to identify
the genuine writer of an online text snippet such as an email, blog, forum post, or chat log. It has
useful applications in manifold domains, for instance, in a verification process to proactively detect
misogynistic, misandrist, xenophobic, and abusive posts on the internet or social networks. The
process assumes that texts can be characterized by sequences of words that agglutinate the functional
and content lyrics of a writer. However, defining an appropriate characterization of text to capture the
unique writing style of an author is a complex endeavor in the discipline of computational linguistics.
Moreover, posts are typically short texts with obfuscating vocabularies that might impact the accuracy
of authorship attribution. The vocabularies include idioms, onomatopoeias, homophones, phonemes,
synonyms, acronyms, anaphora, and polysemy. The method of the regularized deep neural network
(RDNN) is introduced in this paper to circumvent the intrinsic challenges of post-authorship attribution.
It is based on a convolutional neural network, bidirectional long short-term memory encoder,
and distributed highway network. The neural network was used to extract lexical stylometric features
that are fed into the bidirectional encoder to extract a syntactic feature-vector representation. The
feature vector was then supplied as input to the distributed high networks for regularization to
minimize the network-generalization error. The regularized feature vector was ultimately passed
to the bidirectional decoder to learn the writing style of an author. The feature-classification layer
consists of a fully connected network and a SoftMax function to make the prediction. The RDNN
method was tested against thirteen state-of-the-art methods using four benchmark experimental
datasets to validate its performance. Experimental results have demonstrated the effectiveness of the
method when compared to the existing state-of-the-art methods on three datasets while producing
comparable results on one dataset.
