Predicting the popularity of instagram posts for a lifestyle magazine using deep learning
Shaunak De, Abhishek Maity, Vritti Goel, Sanjay S. Shitole, Avik Bhattacharya · 2017
In this paper we use a Deep Neural Network (DNN) trained on data collected from the visual media-sharing social platform Instagram account of a popular Indian lifestyle magazine to predict the popularity of future posts. This predicted popularity of the post can be used to decide advertising rates and measure performance metrics important for publishing strategy decisions. The DNN primarily uses growth rate in subscriber base, tags associated with the post, time of day when the post was made, day of the week, color descriptors of the image, time between current and previous post, popularity of previous post as features for prediction. This covers majority of the causes of variation in popularity. Mini-batch gradient descend method is used to learn the weights in DNN and the objective function is cross-entropy. The network performs acceptable for real world applications and tolerances are within acceptable limits for the application.