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Trending or not? Predictive analysis for youtube videos

Author

Listed:
  • Mohammed Shahid Irshad

    (Banarsidas Chandiwala Institute of Professional Studies, GGSIPU)

  • Adarsh Anand

    (University of Delhi)

  • Mangey Ram

    (Graphic Era Deemed to be University)

Abstract

The internet has brought about significant transformations in communication and human behaviour. It has revolutionised how people connect, express themselves, and socialise. The emergence of social media platforms has turned the virtual world into a tangible reality, enabling global connectivity without barriers. Initially designed to stay in touch with acquaintances and share thoughts, social media platforms have evolved to offer a diverse range of services. These platforms have become thriving marketplaces, influencing consumer behaviour in various ways. Platforms like YouTube have witnessed notable changes in the number and nature of advertisements accompanying videos. Due to the revenue-sharing model based on advertisements, YouTube had to increase its video view count. Additionally, YouTube has introduced video categorisation, with one such category being "trending videos." This proposal utilises existing machine learning techniques like support vector, logistic regression and decision tree to predict whether a video will be categorised as trending, employing supervised machine learning methods. The results are then compared based on their accuracy and precision, providing insights into the effectiveness of the techniques.

Suggested Citation

  • Mohammed Shahid Irshad & Adarsh Anand & Mangey Ram, 2024. "Trending or not? Predictive analysis for youtube 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. 15(4), pages 1568-1579, April.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:4:d:10.1007_s13198-023-02034-8
    DOI: 10.1007/s13198-023-02034-8
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    References listed on IDEAS

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    1. Bhaskar Roy & Debabrata Bera & Somya Nigam & S. K. Upadhyay, 2022. "A study of turbine failure pattern: a model optimization using machine learning," 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. 13(4), pages 1761-1770, August.
    2. Kalpana Jain & Naveen Choudhary, 2022. "Comparative analysis of machine learning techniques for predicting production capability of crop yield," 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. 13(1), pages 583-593, March.
    3. Abhishek Vaish & Rajiv Krishna G. & Akshay Saxena & Dharmaprakash M. & Utkarsh Goel, 2012. "Quantifying Virality of Information in Online Social Networks," International Journal of Virtual Communities and Social Networking (IJVCSN), IGI Global, vol. 4(1), pages 32-45, January.
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