Predicting responses to microblog posts

  • Authors:
  • Yoav Artzi;Patrick Pantel;Michael Gamon

  • Affiliations:
  • University of Washington, Seattle, WA;Microsoft Research, Redmond, WA;Microsoft Research, Redmond, WA

  • Venue:
  • NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  • Year:
  • 2012

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Abstract

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.