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Predicting YouTube success through facial emotion recognition of video thumbnails

In: Handbook of Social Computing

Author

Listed:
  • Peter-Duy-Linh Bui
  • Martin Feldges
  • Max Liebig
  • Fabian Weiland

Abstract

This chapter proposes a correlation analysis and a prediction regarding the success of YouTube video thumbnails based on facial attributes (emotion, ethnicity, age, and gender). For this purpose, all videos created by the 100 most popular German content creators on YouTube are considered. We create a Success Score to measure the success of a YouTube video thumbnail. The Success Score comprises video views, their relative level, and their trend. Using this measurement, the presence of faces within YouTube thumbnails correlates positively with video success. The correlations between video success and facial attributes also differ depending on the video category. In particular, happy faces, for instance, enrich the success of gaming videos, while they reduce success in sports videos. Finally, a classification model is built that can predict YouTube video success using facial attributes from thumbnails.

Suggested Citation

  • Peter-Duy-Linh Bui & Martin Feldges & Max Liebig & Fabian Weiland, 2024. "Predicting YouTube success through facial emotion recognition of video thumbnails," Chapters, in: Peter A. Gloor & Francesca Grippa & Andrea Fronzetti Colladon & Aleksandra Przegalinska (ed.), Handbook of Social Computing, chapter 7, pages 142-158, Edward Elgar Publishing.
    • Handle: RePEc:elg:eechap:21469_7
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    File URL: https://www.elgaronline.com/doi/10.4337/9781803921259.00015
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