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Employing a data mining approach for identification of mobile opinion leaders and their content usage patterns in large telecommunications datasets

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  • Chen, Chih Ping
  • Weng, Ju-Yin
  • Yang, Chin-Sheng
  • Tseng, Fan-Mei

Abstract

Recent technological advances in communication infrastructure, especially those in the realm of mobile services, have drastically impacted how people interact socially. The more ubiquitous and convenient mobile approach has steered away users from the traditional computer-mediated systems. As the mobile trend continues, understanding usage behavior patterns of its users becomes critical to harness the power of this new technology. One particular user group, opinion leaders deemed tremendously influential on the usage behavior of other users, is the main focus of this study. Existing literature on the mobile usage pattern of opinion leaders is limited to theoretical rather than empirical studies. This research attempts to bridge the gap. Drawing from one of the largest Taiwanese telecommunications databases, we try to identify mobile opinion leaders and further cluster their mobile usage patterns by mining the actual data. This study exploits a combination of techniques, including statistics, data mining, and pattern recognition, in our data analysis. Four main characteristics and seven usage patterns of mobile opinion leaders were identified. There are two main contributions of this work. The first contribution is the application of opinion leadership theories applied in the traditional marketplace into mobile services based on a big data system; the second offers a taxonomy to logically analyze each pattern of mobile content usage behavior gathered from mining the data to provide better planning blueprint for future mobile resource consumption.

Suggested Citation

  • Chen, Chih Ping & Weng, Ju-Yin & Yang, Chin-Sheng & Tseng, Fan-Mei, 2018. "Employing a data mining approach for identification of mobile opinion leaders and their content usage patterns in large telecommunications datasets," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 88-98.
    • Handle: RePEc:eee:tefoso:v:130:y:2018:i:c:p:88-98
    DOI: 10.1016/j.techfore.2018.01.014
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    References listed on IDEAS

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    Cited by:

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    3. Thangeda, Rahul & Kumar, Niraj & Majhi, Ritanjali, 2024. "A neural network-based predictive decision model for customer retention in the telecommunication sector," Technological Forecasting and Social Change, Elsevier, vol. 202(C).

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