The analysis of authorial style, termed stylometry, assumes that style is quantifiably measurable for evaluation of distinctive qualities. Stylometry research has yielded several methods and tools over the past 200 years to handle a variety of challenging cases. This survey reviews several articles within five prominent subtasks: authorship attribution, authorship verification, authorship profiling, stylochronometry, and adversarial stylometry. Discussions on datasets, features, experimental techniques, and recent approaches are provided. Further, a current research challenge lies in the inability of authorship analysis techniques to scale to a large number of authors with few text samples. Here, we perform an extensive performance analysis on a corpus of 1,000 authors to investigate authorship attribution, verification, and clustering using 14 algorithms from the literature. Finally, several remaining research challenges are discussed, along with descriptions of various open-source and commercial software that may be useful for stylometry subtasks.

Surveying Stylometry Techniques and Applications

Convolutional neural networks (CNNs) have demonstrated superior capability for extracting information from raw signals in computer vision. Recently, character-level and multi-channel CNNs have exhibited excellent performance for sentence classification tasks. We apply CNNs to large-scale authorship attribution, which aims to determine an unknown text's author among many candidate authors, motivated by their ability to process character-level signals and to differentiate between a large number of classes, while making fast predictions in comparison to state-of-the-art approaches. We extensively evaluate CNN-based approaches that leverage word and character channels and compare them against state-of-the-art methods for a large range of author numbers, shedding new light on traditional approaches. We show that character-level CNNs outperform the state-of-the-art on four out of five datasets in different domains. Additionally, we present the first application of authorship attribution to reddit.

Character-level and Multi-channel Convolutional Neural Networks for Large-scale Authorship Attribution

We address the problem of how to “obfuscate” texts by removing stylistic clues which can identify authorship, whilst preserving (as much as possible) the content of the text. In this paper we combine ideas from “generalised differential privacy” and machine learning techniques for text processing to model privacy for text documents. We define a privacy mechanism that operates at the level of text documents represented as “bags-of-words”—these representations are typical in machine learning and contain sufficient information to carry out many kinds of classification tasks including topic identification and authorship attribution (of the original documents). We show that our mechanism satisfies privacy with respect to a metric for semantic similarity, thereby providing a balance between utility, defined by the semantic content of texts, with the obfuscation of stylistic clues. We demonstrate our implementation on a “fan fiction” dataset, confirming that it is indeed possible to disguise writing style effectively whilst preserving enough information and variation for accurate content classification tasks. We refer the reader to our complete paper [15] which contains full proofs and further experimentation details.

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Generalised Differential Privacy for Text Document Processing

We report on the first large-scale evaluation of author obfuscation approaches built to attack authorship verification approaches: the impact of 3 obfuscators on the performance of a total of 44 authorship verification approaches has been measured and analyzed. The best-performing obfuscator successfully impacts the decision-making process of the authorship verifiers on average in about 47% of the cases, causing them to misjudge a given pair of documents as having been written by “different authors” when in fact they would have decided otherwise if one of them had not been automatically obfuscated. The evaluated obfuscators have been submitted to a shared task on author obfuscation that we organized at the PAN 2016 lab on digital text forensics. We contribute further by surveying the literature on author obfuscation, by collecting and organizing evaluation methodology for this domain, and by introducing performance measures tailored to measuring the impact of author obfuscation on authorship verification.

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Author Obfuscation: Attacking the State of the Art in Authorship Verification.

We briefly report on the four shared tasks organized as part of the PAN 2019 evaluation lab on digital text forensics and authorship analysis. Each task is introduced, motivated, and the results obtained are presented. Altogether, the four tasks attracted 373 registrations, yielding 72 successful submissions. This, and the fact that we continue to invite the submission of software rather than its run output using the TIRA experimentation platform, demarcates a good start into the second decade of PAN evaluations labs.

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Overview of PAN 2019: Bots and Gender Profiling, Celebrity Profiling, Cross-Domain Authorship Attribution and Style Change Detection

In this paper, we revisit author identification research by conducting a new kind of large-scale reproducibility study: we select 15 of the most influential papers for author identification and recruit a group of students to reimplement them from scratch. Since no open source implementations have been released for the selected papers to date, our public release will have a significant impact on researchers entering the field. This way, we lay the groundwork for integrating author identification with information retrieval to eventually scale the former to the web. Furthermore, we assess the reproducibility of all reimplemented papers in detail, and conduct the first comparative evaluation of all approaches on three well-known corpora.

Who Wrote the Web? Revisiting Influential Author Identification Research Applicable to Information Retrieval

Bei einem Mittagessen mit ihren Groseltern merkt Lisa, dass es kein perfektes Essen gibt. Je nach Praferenzen, Voraussetzungen und Gewohnheiten entscheiden sich Menschen fur unterschiedliche Gerichte. Genauso verhalt sich dies bei Algorithmen des maschinellen Lernens. Hier gibt es nicht per se den einen perfekten Algorithmus, sondern abhangig von den zur Verfugung stehenden Daten eignen sich einige Algorithmen besser als andere. Nichts im Leben ist gratis – und genauso wenig weis man ohne jegliche Annahmen oder Untersuchungen welcher Algorithmus der beste fur ein bestimmtes Problem ist!

Maike Elisa Müller

Papers

Who Wrote the Web? Revisiting Influential Author Identification Research Applicable to Information Retrieval

No Free Lunch Theorem