With the growing popularity of social networks, extremely large amount of users routinely post messages about their daily life to online social networking services. In particular, we have observed that family related information, including some very sensitive information, are freely available and easily extracted from Twitter. In this paper, we present a hybrid information retrieval mechanism, namely FamilyID, to identify and extract family related information of a user from his/her microblogs (tweets). The proposed model takes into account part-of-speech tagging, pattern matching, lexical similarity, and semantic similarity of the tweets. Experiment results show that FamilyID provides both high precision and recall. We expect the project to serve as a warning to users that they may have accidentally revealed too much personal/family information to the public. It could also help microblog users to evaluate the amount of information that they have already revealed.