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

H. Mohtasseb, A. Ahmed

Research output: Contribution to journalArticle (journal)peer-review

4 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)1-21
JournalKnowledge and Information Systems
Issue number1
Publication statusPublished - 20 Apr 2012

Research Centres

  • Data and Complex Systems Research Centre
  • Data Science STEM Research Centre


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