People often find useful content on the web via social media. However, it is difficult to manually aggregate the information and recommendations embedded in a torrent of social feeds like email and Twitter. At the same time, the evergrowing size of the web and attempts to spam commercial search engines make it a challenge for users to get search results relevant to their unique background and interests. To address these problems, we propose to let users mine their own social chatter, and thereby extract people, pages and sites of potential interest. This information can be used to effectively personalize web search results. Additionally, our approach leverages social curation, eliminates web spam and improves user privacy. We have built a system called SLANT to automatically mine a user’s email and Twitter feeds and populate four personalized search indices that are used to augment regular web search. We evaluated these indices and found that the small slice of the web indexed using social chatter can produce results that are equally or better liked by users compared to personalized search by a commercial search engine. We find that user satisfaction with search results can be improved by combining the best results from multiple indices
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