1. One or more computer-readable storage media comprising instructions stored thereon that, responsive to execution by a computing device, cause the computing device to perform operations comprising: collecting posts to a social network and responses to each post by a social network community; binning the posts into different post classes based on a number of the responses to each post, the binning comprising normalizing the number of the responses to each post by dividing the number of the responses to each post by a total number of subscribers of the social network community, generating a histogram of the normalized number of responses to each post, applying Otsu thresholding techniques to the histogram to identify one or more boundaries between distributions of the posts in the histogram, and binning at least one of the distributions of the posts into a popular post class that includes posts that received a high number of responses; extracting features from the posts, the extracted features including at least one non-textual feature; and forming a prediction model by applying a learning model to the different classes of posts and the extracted features.
1. One or more computer-readable storage media comprising instructions stored thereon that, responsive to execution by a computing device, cause the computing device to perform operations comprising: collecting posts to a social network and responses to each post by a social network community; binning the posts into different post classes based on a number of the responses to each post, the binning comprising normalizing the number of the responses to each post by dividing the number of the responses to each post by a total number of subscribers of the social network community, generating a histogram of the normalized number of responses to each post, applying Otsu thresholding techniques to the histogram to identify one or more boundaries between distributions of the posts in the histogram, and binning at least one of the distributions of the posts into a popular post class that includes posts that received a high number of responses; extracting features from the posts, the extracted features including at least one non-textual feature; and forming a prediction model by applying a learning model to the different classes of posts and the extracted features. 3. The one or more computer-readable storage media of claim 1 , wherein the popular post class includes approximately 10% of the collected posts.