Paper: Predicting Responses to Microblog Posts

ACL ID N12-1074
Title Predicting Responses to Microblog Posts
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2012

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 viral- ity, reach and effectiveness of messages and ad campaigns on these networks. We propose a discriminative model for predicting the like- lihood of a response or a retweet on the Twit- ter network. The approach uses features de- rived from various sources, such as the lan- guage used in the tweet, the user?s social net- work and history. The feature design process leverages aggregate statistics over the entire social network to balance sparsity and infor- mativeness. We use real-world tweets to train models and empirically show that they are ca- pable of generating accurate predictions for a large number of tweets...