Discovering influencers for marketing in the blogosphere

Yung-Ming Li*, Cheng Yang Lai, Ching Wen Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

56 Scopus citations

Abstract

Discovering influential bloggers will not only allow us to understand better the social activities taking place in the blogosphere, but will also provide unique opportunities for sales and advertising. In this paper, we develop an MIV (marketing influential value) model to evaluate the influential strength and identify the influential bloggers in the blogosphere. We analyze three dimensions of blog characteristics (network-based, content-based, and activeness-based factors) and utilize an artificial neural network (ANN) to discover potential bloggers. Based on peer and official evaluations, the experimental results show that the proposed framework outperforms two social-network-based methods (out-degree and betweenness centrality algorithms) and two content-based mechanisms (review rating and popular author approaches). The proposed framework can be effectively applied to support marketers or advertisers in promoting their products or services.

Original languageEnglish
Pages (from-to)5143-5157
Number of pages15
JournalInformation sciences
Volume181
Issue number23
DOIs
StatePublished - 1 Dec 2011

Keywords

  • Artificial neural network
  • Blogosphere
  • Influential model
  • Social networks
  • Viral marketing

Fingerprint Dive into the research topics of 'Discovering influencers for marketing in the blogosphere'. Together they form a unique fingerprint.

Cite this