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2 datasets found
  1. PAN22 Authorship Analysis: Authorship Verification

    • zenodo.org
    Updated Mar 8, 2022
  2. o

    PAN22 Authorship Analysis: Style Change Detection

    • explore.openaire.eu
    • zenodo.org
    Updated Mar 7, 2022
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Stamatatos, Efstathios; Kredens, Krzysztof; Pezik, Piotr; Heini, Annina; Kestemont, Mike; Bevendorff, Janek; Potthast, Martin; Stein, Benno (2022). PAN22 Authorship Analysis: Authorship Verification [Dataset]. http://doi.org/10.5281/zenodo.6337137
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PAN22 Authorship Analysis: Authorship Verification

Dataset updated
Mar 8, 2022
Dataset provided by
University of Antwerphttp://www.ua.ac.be/
Aston Universityhttp://www.aston.ac.uk/
University of the Aegeanhttp://www.aegean.gr/
Bauhaus-Universität Weimarhttp://www.uni-weimar.de/
Leipzig Universityhttp://www.uni-leipzig.de/
Authors
Stamatatos, Efstathios; Kredens, Krzysztof; Pezik, Piotr; Heini, Annina; Kestemont, Mike; Bevendorff, Janek; Potthast, Martin; Stein, Benno
Description

Task

Authorship verification is the task of deciding whether two texts have been written by the same author based on comparing the texts' writing styles. In previous editions of PAN, we explored the effectiveness of authorship verification technology in several languages and text genres. In the two most recent editions, cross-domain authorship verification using fanfiction texts was examined. Despite certain differences between fandoms, the task of cross-fandom authorship verification has proved to be relatively feasible. In the current edition, we focus on more challenging scenarios where each author verification case considers two texts that belong to different DTs (cross-DT authorship verification). This will allow us to study the ability of stylometric approaches to capture authorial characteristics that remain stable across DTs even when very different forms of expression are imposed by the DT norms.

Based on a new corpus in English, we provide cross-DT authorship verification cases using the following DTs:

  • Essays
  • Emails
  • Text messages
  • Business memos

The corpus comprises texts of around 100 individuals. All individuals have similar age (18-22) and are native English speakers. The topic of text samples is not restricted while the level of formality can vary within a certain DT (e.g., text messages may be addressed to family members or non-familial acquaintances).

More information at: Authorship Verification 2022

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