Abstract
Enhancing information retrieval systems with the ability to take the writing style of people into account opens the door for a number of applications. For example, one can link articles by authorships that can help identifying authors who generate hoaxes and deliberate misinformation in news stories, distributed across different platforms. Authorship verification (AV) is a technique that can be used for this purpose. AV deals with the task to judge, whether two or more documents stem from the same author. The majority of existing AV approaches relies on machine learning concepts based on explicitly defined stylistic features and complex models that involve a fair amount of parameters. Moreover, many existing AV methods are based on explicit thresholds (needed to accept or reject a stated authorship), which are determined on training corpora. We propose a novel parameter-free AV approach, which derives its thresholds for each verification case individually and enables AV in the absence of explicit features and training corpora. In an experimental setup based on eight evaluation corpora (each one from another language) we show that our approach yields competitive results against the current state of the art and other noteworthy AV baselines.
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Notes
- 1.
One-Class Compression Authorship Verifier.
- 2.
PAN is a series of scientific events and shared tasks on digital text forensics [28].
- 3.
Homotopy-based Classification is used in face recognition, where the goal is to measure the contribution of known faces in the generation of an unknown face [6].
- 4.
Available under http://pan.webis.de.
- 5.
Area Under the ROC-Curve.
- 6.
Prediction by Partial Matching. Note that d refers to the order of the PPM model.
- 7.
We use Hathcock’s library (https://github.com/adamhathcock/sharpcompress).
- 8.
“Large Text Compression Benchmark” (http://mattmahoney.net/dc/text.html).
- 9.
Note that this measure is not a metric, as all conditions a metric must satisfy (identity, symmetry and triangle inequality) are not met.
- 10.
All corpora are available upon request under http://bit.do/ECIR_2018.
- 11.
Evidence for this can be seen by comparing the number of published papers across different bibliographic databases such as Google Scholar or Microsoft Academic.
- 12.
We used Van Asch’s script (http://www.clips.uantwerpen.be/scripts/art).
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Acknowledgments
This work was supported by the German Federal Ministry of Education and Research (BMBF) under the project “DORIAN” (Scrutinise and thwart disinformation). We would like to thank Christian Winter and Felix Mayer for their valuable reviews that helped to improve the quality of this paper.
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Halvani, O., Graner, L., Vogel, I. (2018). Authorship Verification in the Absence of Explicit Features and Thresholds. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds) Advances in Information Retrieval. ECIR 2018. Lecture Notes in Computer Science(), vol 10772. Springer, Cham. https://doi.org/10.1007/978-3-319-76941-7_34
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