document: Finding the right facts in the crowd : factoid question answering over social media Community Question Answering has emerged as a popular and effective paradigm for a wide range of information needs . For example , to find out an obscure piece of trivia , it is now possible and even very effective to post a question on a popular community QA site such as Yahoo ! Answers , and to rely on other users to provide answers , often within minutes . The importance of such community QA sites is magnified as they create archives of millions of questions and hundreds of millions of answers , many of which are invaluable for the information needs of other searchers . However , to make this immense body of knowledge accessible , effective answer retrieval is required . In particular , as any user can contribute an answer to a question , the majority of the content reflects personal , often unsubstantiated opinions . A ranking that combines both relevance and quality is required to make such archives usable for factual information retrieval . This task is challenging , as the structure and the contents of community QA archives differ significantly from the web setting . To address this problem we present a general ranking framework for factual information retrieval from social media . Results of a large scale evaluation demonstrate that our method is highly effective at retrieving well-formed , factual answers to questions , as evaluated on a standard factoid QA benchmark . We also show that our learning framework can be tuned with the minimum of manual labeling . Finally , we provide result analysis to gain deeper understanding of which features are significant for social media search and retrieval . Our system can be used as a crucial building block for combining results from a variety of social media content with general web search results , and to better integrate social media content for effective information access .
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