Abstract
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.
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Notes
- 1.
Interestingly, Collberg et al.’s study itself has been challenged for lack of rigor and has been reproduced more thoroughly: http://cs.brown.edu/~sk/Memos/Examining-Reproducibility/.
- 2.
Materials and code of this study are available at www.uni-weimar.de/medien/webis/ publications and the latest versions of the code in its GitHub repositories at www.github.com/pan-webis-de (for a convenient overview, see www.github.com/search?q=ECIR+2016+user:pan-webis-de).
- 3.
Confer the repository of the reimplementation of Seroussi et al.’s approach to follow up on this.
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Acknowledgements
This study was supported by the German National Academic Foundation (German: Studienstiftung des deutschen Volkes). The foundation helped to recruit students among its scholars and organized our auditing workshop as part of its 2015 summer academy in La Colle-sur-Loup, France. We thank the foundation for their generous support. Our special thanks go to Dorothea Trebesius, Matthias Frenz, and Martina Rothmann-Stang who provided for our every need at the workshop.
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Potthast, M. et al. (2016). Who Wrote the Web? Revisiting Influential Author Identification Research Applicable to Information Retrieval. In: Ferro, N., et al. Advances in Information Retrieval. ECIR 2016. Lecture Notes in Computer Science(), vol 9626. Springer, Cham. https://doi.org/10.1007/978-3-319-30671-1_29
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