Multi-branch LSTM encoded latent features with CNN-LSTM for Youtube popularity prediction

As digital media grows, there is an increasing demand for engaging content that can captivate audiences. Along with that, the monetary conversion of those engaging videos is also increased. This leads to the way for more content-driven videos, which can generate revenue. YouTube is the most popular platform which shared the revenue from advertisement to video publisher. This paper focuses on the work of video popularity prediction of the YouTube data. The idea of mapping the video features into low-dimensional space to get the latent features is presented. This mapping is achieved by a novel multi-branch child-parent Long Short Term Memory (LSTM) network. These latent features train the fused Convolutional Neural Network (CNN) with LSTM to predict the popularity of unseen videos on the trained deep learning network. We compared our results against Linear Regression (LR), Support Vector Regression (SVR) and Fully Convolutional Networks (FCN) with LSTM. A significant improvement with a 50% reduction in MAE and a 0.61% increase in the coefficient of determination (R²) has been observed by the proposed Multi branch LSTM encoded features with a fused deep learning predictor (MLEF-DL predictor) when compared to existing methods.