Discovering influencers for marketing in the blogosphere

  • Authors:

  • Yung-Ming Li;Cheng-Yang Lai;Ching-Wen Chen

  • Affiliations:

  • Institute of Information Management, National Chiao Tung University, Hsinchu 300, Taiwan;Institute of Information Management, National Chiao Tung University, Hsinchu 300, Taiwan;Institute of Information Management, National Chiao Tung University, Hsinchu 300, Taiwan

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2011

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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.