TY - JOUR
T1 - Optimizing Prediction of YouTube Video Popularity Using XGBoost
AU - Nisa, Meher UN
AU - Mahmood, Danish
AU - Ahmed, Ghufran
AU - Khan, Suleman
AU - Mohammed, Mazin Abed
AU - Damaševičius, Robertas
PY - 2021/11/28
Y1 - 2021/11/28
N2 - YouTube is a source of income for many people, and therefore a video’s popularity ultimately becomes the top priority for sustaining a steady income, provided that the popularity of videos remains the highest. Analysts and researchers use different algorithms and models to predict the maximum viewership of popular videos. This study predicts the popularity of such videos using the XGBoost model, considering features selection, fusion, min-max normalization and some precision parameters such as gamma, eta, learning_rate etc. The XGBoost gives 86% accuracy and 64% precision. Moreover, the Tuned XGboost also shows enhanced accuracy and precision. We have also analyzed the classification of unpopular videos for a comparison with our results. Finally, cross-validation methods are also used to evaluate certain combination of parameter’s values to validate our claims. Based on the obtained results, it can be said that our proposed models and techniques are very useful and can precisely and accurately predict the popularity of YouTube videos.
AB - YouTube is a source of income for many people, and therefore a video’s popularity ultimately becomes the top priority for sustaining a steady income, provided that the popularity of videos remains the highest. Analysts and researchers use different algorithms and models to predict the maximum viewership of popular videos. This study predicts the popularity of such videos using the XGBoost model, considering features selection, fusion, min-max normalization and some precision parameters such as gamma, eta, learning_rate etc. The XGBoost gives 86% accuracy and 64% precision. Moreover, the Tuned XGboost also shows enhanced accuracy and precision. We have also analyzed the classification of unpopular videos for a comparison with our results. Finally, cross-validation methods are also used to evaluate certain combination of parameter’s values to validate our claims. Based on the obtained results, it can be said that our proposed models and techniques are very useful and can precisely and accurately predict the popularity of YouTube videos.
KW - YouTube videos
KW - feature fusion
KW - video popularity prediction
KW - social networks
KW - Video popularity prediction
KW - Social networks
KW - Feature fusion
UR - http://www.scopus.com/inward/record.url?scp=85119958392&partnerID=8YFLogxK
U2 - 10.3390/electronics10232962
DO - 10.3390/electronics10232962
M3 - Article
SN - 2079-9292
VL - 10
JO - Electronics
JF - Electronics
IS - 23
M1 - 2962
ER -