A Hybrid Swarm Intelligence Approach for Blog Success Prediction

DOI

10.2991/ijcis.d.190423.001How to use a DOI?

Keywords

Blog success model; Prediction mechanism; Particle swarm optimization; Self-organizing map

Abstract

Successful blogs receive high ratings and generate marketing value. What factors contribute to the success of a blog and how to predict its success level are questions worth discussing. A hybrid swam intelligence approach is proposed in this study to predict blog success level. First, this study develops a research model of blog success with six factors from content, technology, and social views of point, which include currentness, design, reliability, security, interaction, and connectivity. A questionnaire is designed based on the blog success model. Two hundred ten valid samples are collected from Internet users with experience in using or creating blogs. A hybrid approach combining particle swarm optimization (PSO) and self-organizing map (SOM) is proposed to predict blog success level. The results of 10-fold validation are examined to compare the hybrid PSO–SOM approach with the results from three classifiers: C5.0, classification and regression trees (CARTs), and support vector machine (SVM). For blog success prediction, the results indicate the PSO–SOM approach demonstrates higher accuracy among these methods.

Copyright

© 2019 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Chi-I Hsu
AU  - Shelly P. J. Wu
AU  - Chaochang Chiu
PY  - 2019
DA  - 2019/04/29
TI  - A Hybrid Swarm Intelligence Approach for Blog Success Prediction
JO  - International Journal of Computational Intelligence Systems
SP  - 571
EP  - 579
VL  - 12
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.190423.001
DO  - 10.2991/ijcis.d.190423.001
ID  - Hsu2019
ER  -