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Improved score aggregation for authorship verification

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Abstract

The Impostors method is one of the most successful solvers of author verification problems. Given a pair of texts, it aims to find whether the same author wrote them or not. This paper describes a proposed approach with the primary objective of achieving a higher classification accuracy. This higher accuracy is achieved by modifying the vector representations of input texts, such that the effect of them possibly being in different domains is reduced. Such vector modification factors are obtained by the addition of a computational step that empirically estimates the expected difference, or ratio, between the questioned texts’ similarity scores against their in-domain samples. Our evaluation confirms that our proposed approach is capable of achieving higher classification accuracy than the original method. Despite the size of the evaluation dataset, some of the increases in the classification accuracy are large enough to allow for observing statistically significant, very significant, and highly significant gains.

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Availability of data and materials

The tool used to extract the features from the considered dataset is available at: https://gitlab.com/mmaakh/fextractor The dataset supporting the conclusions of this article is available in the PAN Data repository, https://pan.webis.de/data.html.

Notes

  1. The word shapes are based on three properties: characters case (e.g., lower/upper case), characters type (whether it is a letter or a number), and words length. For example, the word “School” is represented as the gram “Cccccc,” “2017” is represented as the gram “NNNN,” and “x86” is represented as the gram “cNN.”

  2. Part of speech tags such as NN, NNS, NNP, VB, VBD, and VBG. For example, if the word “school” existed in a text as a noun, then it would be represented as the gram “NN.” A comprehensive list of such tags can be found in https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html.

  3. Combinations of words and their respective POS tags. For example, if the word “saw” was a noun, then it would be represented as the gram “saw-NN,” and if the word was a verb, then it would be represented as the gram “saw-VBD.”

Abbreviations

AA:

Author attribution

AP:

Author profiling

AR:

Approximate randomization

AV:

Author verification

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Authors and Affiliations

  1. Electrical Engineering and Computer Science Department, Khalifa University, Abu Dhabi, UAE

    Mahmoud Khonji

  2. School of Computer Science, Mohammed VI Polytechnic University, Ben Guerir, Morocco

    Youssef Iraqi & Loubna Mekouar

Authors
  1. Mahmoud Khonji
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  2. Youssef Iraqi
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  3. Loubna Mekouar
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Correspondence to Youssef Iraqi.

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Khonji, M., Iraqi, Y. & Mekouar, L. Improved score aggregation for authorship verification. Knowl Inf Syst 65, 1317–1336 (2023). https://doi.org/10.1007/s10115-022-01798-y

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