Stylometric authorship attribution of collaborative documents

Edwin Dauber, Rebekah Overdorf, Rachel Greenstadt

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    Stylometry is the study of writing style based on linguistic features and is typically applied to authorship attribution problems. In this work, we apply stylometry to a novel dataset of multi-authored documents collected from Wikia using both relaxed classification with a support vector machine (SVM) and multi-label classification techniques. We define five possible scenarios and show that one, the case where labeled and unlabeled collaborative documents by the same authors are available, yields high accuracy on our dataset while the other, more restrictive cases yield lower accuracies. Based on the results of these experiments and knowledge of the multi-label classifiers used, we propose a hypothesis to explain this overall poor performance. Additionally, we perform authorship attribution of pre-segmented text from the Wikia dataset, and show that while this performs better than multi-label learning it requires large amounts of data to be successful.

    Original languageEnglish (US)
    Title of host publicationCyber Security Cryptography and Machine Learning - 1st International Conference, CSCML 2017, Proceedings
    EditorsShlomi Dolev, Sachin Lodha
    PublisherSpringer Verlag
    Number of pages21
    ISBN (Print)9783319600796
    StatePublished - 2017
    Event1st International Conference on Cyber Security Cryptography and Machine Learning, CSCML 2017 - Beer-Sheva, Israel
    Duration: Jun 29 2017Jun 30 2017

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume10332 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Conference1st International Conference on Cyber Security Cryptography and Machine Learning, CSCML 2017


    • Authorship attribution
    • Machine learning
    • Multi-label learning
    • Stylometry

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • General Computer Science


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