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.