Google Research

Question Identification on Twitter, Accepted by CIKM 2011

  • Baichuan Li
  • Xiance Si
  • Michael R. Lyu
  • Irwin King
  • Edward Y. Chang
Proceedings of the 20th ACM international conference on Information and knowledge management, ACM, New York, NY, USA (2011)

Abstract

In this paper, we investigate the novel problem of auto- matic question identification in the microblog environment. It contains two steps: detecting tweets that contain ques- tions (we call them “interrogative tweets”) and extracting the tweets which really seek information or ask for help (so called “qweets”) from interrogative tweets. To detect inter- rogative tweets, both traditional rule-based approach and state-of-the-art learning-based method are employed. To extract qweets, context features like short urls and Tweet- specific features like Retweets are elaborately selected for classification. We conduct an empirical study with sampled one hour’s English tweets and report our experimental re- sults for question identification on Twitter.

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