VIDEO POPULARITY PREDICTION USING STACKED BILSTM LAYERS - Sangwan, Neeti; Bhatnagar, Vishal (Malaysian Journal of Computer Science, 2021)
Social media is now not only limited to being a life event sharing platform, but it also has evolved as a monetary medium. Advertisements showing on popular videos may result in more sales conversion. So it is of utmost interest to predict the popularity of videos before uploading it on the platform. In this research article, we propose a deep learning algorithm to predict the popularity of YouTube videos. With the content and temporal features of the YouTube videos dataset, we use a novel stack of deep learning layers. We validate the approach with state-of-the-art methods and prove that the proposed complex stacked architecture gives more accurate and stable results. Results are also tested for short duration prediction with a different number of reference days after video publishing.