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        <title>Topic or Style? Exploring the Most Useful Features for Authorship Attribution</title>
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    <abstract>Approaches to authorship attribution, the task of identifying the author of a document, are based on analysis of individuals’ writing style and/or preferred topics. Although the problem has been widely explored, no previous studies have analysed the relationship between dataset characteristics and effectiveness of different types of features. This study carries out an analysis of four widely used datasets to explore how different types of features affect authorship attribution accuracy under varying conditions. The results of the analysis are applied to authorship attribution models based on both discrete and continuous representations. We apply the conclusions from our analysis to an extension of an existing approach to authorship attribution and outperform the prior state-of-the-art on two out of the four datasets used.</abstract>
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%0 Conference Proceedings
%T Topic or Style? Exploring the Most Useful Features for Authorship Attribution
%A Sari, Yunita
%A Stevenson, Mark
%A Vlachos, Andreas
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F sari-etal-2018-topic
%X Approaches to authorship attribution, the task of identifying the author of a document, are based on analysis of individuals’ writing style and/or preferred topics. Although the problem has been widely explored, no previous studies have analysed the relationship between dataset characteristics and effectiveness of different types of features. This study carries out an analysis of four widely used datasets to explore how different types of features affect authorship attribution accuracy under varying conditions. The results of the analysis are applied to authorship attribution models based on both discrete and continuous representations. We apply the conclusions from our analysis to an extension of an existing approach to authorship attribution and outperform the prior state-of-the-art on two out of the four datasets used.
%U https://aclanthology.org/C18-1029
%P 343-353

Markdown (Informal)

[Topic or Style? Exploring the Most Useful Features for Authorship Attribution](https://aclanthology.org/C18-1029) (Sari et al., COLING 2018)

• Topic or Style? Exploring the Most Useful Features for Authorship Attribution (Sari et al., COLING 2018)

ACL

• Yunita Sari, Mark Stevenson, and Andreas Vlachos. 2018. Topic or Style? Exploring the Most Useful Features for Authorship Attribution. In Proceedings of the 27th International Conference on Computational Linguistics, pages 343–353, Santa Fe, New Mexico, USA. Association for Computational Linguistics.