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.
Artzi et al. REF predict whether a message will elicit a user response in Twitter based on a discriminative model, and they explore various sources as features, such as the language used in the tweet, the user's social network and history.
Predicting Responses to Microblog Posts
NAACL
Computer Science