Microblogging networks serve as vehicles for reaching and influencing users. Predicting whether a message will elicit a user response opens the possibility of maximizing the virality,
reach and effectiveness of messages and ad campaigns on these networks. We propose a discriminative model for predicting the likelihood of a response or a retweet on the Twitter
network. The approach uses features derived from various sources, such as the language
used in the tweet, the user’s social network and history. The feature design process leverages aggregate statistics over the entire social network to balance sparsity and Informativeness. We use real-world tweets to train models and empirically show that they are capable of generating accurate predictions for a large number of tweets.