Image Popularity Prediction Over Time For the Span Of 30 Days Using Machine learning Techniques

Abstract

The popularity of social media content define its destiny: some of the uploaded content get famous among people within minutes while others just get completely unnoticed. But why is this so? This work addresses this question, discusses all the features related to social content that is responsible for its popularity or negligence, and proposes a system to predict the popularity of the content for the span of 30 days before actually uploading the content on any social media platform. There are some common features in the social content that gets fame, in this research work we have evaluated the effect of different features on popularity. The proposed model predicts the popularity score in the form of the number of views for the next 30 days after uploading the content. The content popularity score can be used by companies to improve their marketing strategies, targeting the right audience sagaciously, managing the resources efficiently, and making the strategical decisions. In the paper, the detailed methodology is discussed to design a model that can perform the task of Image Popularity Prediction (IPP) efficiently. A critical analysis is also performed on the results obtained from single features, combinational features, and features obtained by applying different techniques. This research work manifests that the features related to the image context i.e user features and photo features outperform other features related to the content.