Knowledge and Information Systems

, Volume 31, Issue 1, pp 1–21

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

Regular Paper

DOI: 10.1007/s10115-011-0398-0

Cite this article as:
Mohtasseb, H. & Ahmed, A. Knowl Inf Syst (2012) 31: 1. doi:10.1007/s10115-011-0398-0


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.


Blog mining Authorship identification User representation Group extraction Profile modeling 

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.School of Computer ScienceUniversity of LincolnLincolnUK

Personalised recommendations