A New Approach for Predicting Popularity of Videos Using Social Media and Social Media Posts


A New Approach for Predicting Popularity of Videos Using Social Media and Social Media Posts – This paper identifies the need for a robust and accurate automated classification of the content of social media to improve the likelihood of their user engagement. We propose a novel algorithm to discover the content of a social media post by estimating its content from a large corpus of post descriptions. The corpus of short posts contains short descriptions of users, but the posts contained in the same posts contain similar content, which is very useful in finding users. We show that this task can be extended to the cases where content is a combination of short descriptions and content from real posts. We show how this can be used for classifying users’ content using a social media dataset of videos and find the most likely users. We also discuss how this content classification helps to improve the performance of systems that need to find similar users. In addition, we present some practical applications in the context of prediction of user engagement in social media.

We propose a novel method for embedding a large set of unsupervised data into a single latent variable. In this work, we first show that the unsupervised sparse estimation of features via learning over the unsupervised data is possible without requiring any supervised learning. We then show that the sparse estimation learning method is much more efficient than the sparse estimation learned through unsupervised learning. Our method, as well as some new ones, are available in our paper and we have implemented it on a single Ubuntu 15.04 operating system.

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A New Approach for Predicting Popularity of Videos Using Social Media and Social Media Posts

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  • Learning from Continuous Events with the Gated Recurrent Neural Network

    The Deconvolutional Dimension for Discrete HashingWe propose a novel method for embedding a large set of unsupervised data into a single latent variable. In this work, we first show that the unsupervised sparse estimation of features via learning over the unsupervised data is possible without requiring any supervised learning. We then show that the sparse estimation learning method is much more efficient than the sparse estimation learned through unsupervised learning. Our method, as well as some new ones, are available in our paper and we have implemented it on a single Ubuntu 15.04 operating system.


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