Similarity Learning for Authorship Verification in Social Media

Abstract

Authorship verification tries to answer the question if two documents with unknown authors were written by the same author or not. A range of successful technical approaches has been proposed for this task, many of which are based on traditional linguistic features such as n-grams. These algorithms achieve good results for certain types of written documents like books and novels. Forensic authorship verification for social media, however, is a much more challenging task since messages tend to be relatively short, with a large variety of different genres and topics. At this point, traditional methods based on features like n-grams have had limited success. In this work, we propose a new neural network topology for similarity learning that significantly improves the performance on the author verification task with such challenging data sets.

arXiv comment5 pages, 3 figures, 1 table, presented on ICASSP 2019 in Brighton, UK

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Cited by in corpus (6)

• POSNoise: An Effective Countermeasure Against Topic Biases in Authorship Analysis
• Deep Bayes Factor Scoring for Authorship Verification
• Grammar as a Behavioral Biometric: Using Cognitively Motivated Grammar Models for Authorship Verification
• LG4AV: Combining Language Models and Graph Neural Networks for Author Verification
• Forging the Forger: An Attempt to Improve Authorship Verification via Data Augmentation
• Idiosyncratic but not Arbitrary: Learning Idiolects in Online Registers Reveals Distinctive yet Consistent Individual Styles