• Mar 27, 2019 News!Good News! All papers from Volume 9, Number 1 have been indexed by Scopus!   [Click]
  • Mar 30, 2019 News!Vol.9, No.2 has been published with online version.   [Click]
  • Jan 11, 2019 News!The papers published in Vol.9, No.1 have all received dois from Crossref.
General Information
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2015 Vol. 5(2): 127-131 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2015.V5.495

Finding Influential Bloggers

Bogdan Gliwa and Anna Zygmunt
Abstract—Blogging is a popular way of expressing opinions and discussing topics. Bloggers demonstrate different levels of commitment and most interesting are influential bloggers. Around such bloggers, the groups are forming, which concentrate users sharing similar interests. Finding such bloggers is an important task and has many applications e.g. marketing, business, politics. Influential ones affect others which is related to the process of diffusion. However, there is no objective way to telling which blogger is more influential. Therefore, researchers take into consideration different criteria to assess bloggers (e.g. SNA centrality measures). In this paper we propose new, efficient method for influential bloggers discovery which is based on relation of commenting in blogger’s thread and is defined on bloggers level. Next, we compare results with other, comparative method proposed by Agarwal et al. called iFinder which is based on links between posts.

Index Terms—Blogosphere, influential bloggers, social media, social network analysis.

The authors are with AGH University of Science and Technology, Cracow, Poland (e-mail: bgliwa@agh.edu.pl, azygmunt@agh.edu.pl).


Cite: Bogdan Gliwa and Anna Zygmunt, "Finding Influential Bloggers," International Journal of Machine Learning and Computing vol. 5, no. 2, pp. 127-131, 2015.

Copyright © 2008-2019. International Journal of Machine Learning and Computing. All rights reserved.
E-mail: ijmlc@ejournal.net