{"id":164517,"date":"2013-07-01T00:00:00","date_gmt":"2013-07-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/a-crowd-powered-socially-embedded-search-engine\/"},"modified":"2018-10-16T20:20:17","modified_gmt":"2018-10-17T03:20:17","slug":"a-crowd-powered-socially-embedded-search-engine","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/a-crowd-powered-socially-embedded-search-engine\/","title":{"rendered":"A Crowd-Powered Socially Embedded Search Engine"},"content":{"rendered":"<div class=\"asset-content\">\n<p>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 \u201cfriendsourced\u201d 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.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>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 \u201cfriendsourced\u201d answers with crowdsourced answers. By analyzing [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13554,13555],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-164517","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-human-computer-interaction","msr-research-area-search-information-retrieval","msr-locale-en_us"],"msr_publishername":"AAAI","msr_edition":"Proceedings of ICWSM 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