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Modeling and characterizing viewers of You Tube videos

Author

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  • Niyati Aggrawal

    (Jaypee Institute of Information Technology)

  • Anuja Arora

    (Jaypee Institute of Information Technology)

  • Adarsh Anand

    (University of Delhi)

Abstract

All the viewers of an online video do not watch a video at the same time. Consequently, on the basis of the behavioral measure of an individual who is moderately watching the videos earlier than others, viewers have been classified into viewer categories. Viewers’ categorization is much needed and has to be developed as viewers can lend a hand in targeting prospects for an online video and predict the continued sharing of the video. As per internet market literature, understanding and predicting view counts has not only resulted in generation of more traffic but has also acted as popularity metric for videos. Thereby, in the present work, based on analogy with a marketing science model we have studied the behavioral hypothesis for the modeling and quantification is offered in terms of varied and yet connected classes of viewers. With view count data on four YouTube videos, we have examined the diffusion of these videos over the time and illustrated the usefulness of the viewer categorization.

Suggested Citation

  • Niyati Aggrawal & Anuja Arora & Adarsh Anand, 2018. "Modeling and characterizing viewers of You Tube videos," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(2), pages 539-546, April.
    • Handle: RePEc:spr:ijsaem:v:9:y:2018:i:2:d:10.1007_s13198-018-0700-6
    DOI: 10.1007/s13198-018-0700-6
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    References listed on IDEAS

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    1. Frank M. Bass, 2004. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 50(12_supple), pages 1825-1832, December.
    2. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    3. Anjana Susarla & Jeong-Ha Oh & Yong Tan, 2012. "Social Networks and the Diffusion of User-Generated Content: Evidence from YouTube," Information Systems Research, INFORMS, vol. 23(1), pages 23-41, March.
    4. Frank M. Bass, 2004. "Comments on "A New Product Growth for Model Consumer Durables The Bass Model"," Management Science, INFORMS, vol. 50(12_supple), pages 1833-1840, December.
    5. Bass, Frank M, 1980. "The Relationship between Diffusion Rates, Experience Curves, and Demand Elasticities for Consumer Durable Technological Innovations," The Journal of Business, University of Chicago Press, vol. 53(3), pages 51-67, July.
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    Cited by:

    1. Taneja, Anu & Arora, Anuja, 2019. "Modeling user preferences using neural networks and tensor factorization model," International Journal of Information Management, Elsevier, vol. 45(C), pages 132-148.

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