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Stylometry is a form of authorship attribution that relies on the linguistic information to attribute documents of unknown authorship based on the writing styles of a suspect set of authors. This paper focuses on the cross-domain subproblem where the known and suspect documents differ in the setting in which they were created. Three distinct domains, Twitter feeds, blog entries, and Reddit comments, are explored in this work. We determine that state-of-the-art methods in stylometry do not perform as well in cross-domain situations (34.3% accuracy) as they do in in-domain situations (83.5% accuracy) and propose methods that improve performance in the cross-domain setting with both feature and classification level techniques which can increase accuracy to up to 70%. In addition to testing these approaches on a large real world dataset, we also examine real world adversarial cases where an author is actively attempting to hide their identity. Being able to identify authors across domains facilitates linking identities across the Internet making this a key security and privacy concern; users can take other measures to ensure their anonymity, but due to their unique writing style, they may not be as anonymous as they believe.

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Stylometry is a form of authorship attribution that relies on the linguistic information found in a document. While there has been significant work in stylometry, most research focuses on the closed-world problem where the author of the document is in a known suspect set. For open-world problems where the author may not be in the suspect set, traditional classification methods are ineffective. This paper proposes the "classify-verify" method that augments classification with a binary verification step evaluated on stylometric datasets. This method, which can be generalized to any domain, significantly outperforms traditional classifiers in open-world settings and yields an F1-score of 0.87, comparable to traditional classifiers in closed-world settings. Moreover, the method successfully detects adversarial documents where authors deliberately change their styles, a problem for which closed-world classifiers fail.

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