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Two-layered Blogger identification model integrating profile and instance-based methods

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Abstract

This paper introduces a two-layered framework that improves the result of authorship identification within larger sample numbers of bloggers as compared with earlier work. Previous studies are mainly divided into two categories: profile-based and instance-based methods. Each of these approaches has its advantages and limitations. The two-layered framework presented here integrates the two previous approaches and presents a new solution to a key problem in authorship identification, namely the drop in accuracy experienced as the number of authors increases. The paper begins by illustrating the regular instance-based core model and the investigated features. It then introduces a new psycholinguistic profile representation of authors, presents similarity grouping extraction over profiles, and applies blogger identification utilizing the two-layered approach. The results confirm the improvement introduced by the proposed two-layered approach against our regular classifier, as well as a selected baseline, for an extended number of users.

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Authors and Affiliations

  1. School of Computer Science, University of Lincoln, Lincoln, LN6 7TS, UK

    Haytham Mohtasseb & Amr Ahmed

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  1. Haytham Mohtasseb
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  2. Amr Ahmed
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Correspondence to Haytham Mohtasseb.

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Mohtasseb, H., Ahmed, A. Two-layered Blogger identification model integrating profile and instance-based methods. Knowl Inf Syst 31, 1–21 (2012). https://doi.org/10.1007/s10115-011-0398-0

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