A crowd-powered socially embedded search engine

People have always asked questions of their friends, but now, with social media, they can broadcast their questions to their entire social network. In this paper we study the re-plies received via Twitter question asking, and use what we learn to create a system that augments naturally occurring “friendsourced ” answers with crowdsourced answers. By analyzing of thousands of public Twitter questions and an-swers, we build a picture of which questions receive an-swers and the content of their answers. Because many ques-tions seek subjective responses but go unanswered, we use crowdsourcing to augment the Twitter question asking ex-perience. We deploy a system that uses the crowd to identi-fy question tweets, create candidate replies, and vote on the best reply from among different crowd- and friend-generated answers. We find that crowdsourced answers are similar in nature and quality to friendsourced answers, and that almost a third of all question askers provided unsolicit-ed positive feedback upon receiving answers from this novel information agent