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Predicting the Popularity of YouTube Videos: A Data-Driven Approach

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Advances in Computational Intelligence Systems (UKCI 2023)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1453))

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Abstract

This paper presents a data-driven approach to predict the popularity of YouTube videos. The existing conducted studies focus on identifying whether the video is trending currently or after a month of publishing. Where this study aims to leverage a comprehensive dataset of YouTube videos, analyze various features and employ machine-learning techniques to forecast next-hour video popularity accurately. By considering factors such as video metadata and engagement metrics, we develop predictive models that can assist content creators and marketers in understanding the likelihood of a video’s success on the platform. The results demonstrate the efficacy of our approach and provide practical insights into predicting YouTube video popularity.

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Notes

  1. 1.

    https://developers.google.com/youtube/v3/docs.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Manchester, Manchester, M13 9PL, UK

    Alaa Aljamea & Xiao-Jun Zeng

  2. Department of Computer Science, CCSIT, King Faisal University, AlAhsa, 31982, South Africa

    Alaa Aljamea

Authors
  1. Alaa Aljamea
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  2. Xiao-Jun Zeng
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Corresponding author

Correspondence to Alaa Aljamea .

Editor information

Editors and Affiliations

  1. Aston University, Birmingham, UK

    Nitin Naik

  2. Cardiff Metropolitan University, Cardiff, UK

    Paul Jenkins

  3. Aston University, Birmingham, UK

    Paul Grace

  4. Northumbria University, Newcastle upon Tyne, UK

    Longzhi Yang

  5. Devi Ahilya University, Indore, India

    Shaligram Prajapat

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Aljamea, A., Zeng, XJ. (2024). Predicting the Popularity of YouTube Videos: A Data-Driven Approach. In: Naik, N., Jenkins, P., Grace, P., Yang, L., Prajapat, S. (eds) Advances in Computational Intelligence Systems. UKCI 2023. Advances in Intelligent Systems and Computing, vol 1453. Springer, Cham. https://doi.org/10.1007/978-3-031-47508-5_48

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