Social search or a social search engine is a type of web search that takes into account the Social Graph of the person initiating the search query. When applied to web search this Social-Graph approach to relevance is in contrast to established algorithmic or machine-based approaches where relevance is determined by analyzing the text of each document or the link structure of the documents.[1] Search results produced by social search engine give more visibility to content created or touched by users in the Social Graph.
Social search takes many forms, ranging from simple shared bookmarks or tagging of content with descriptive labels to more sophisticated approaches that combine human intelligence with computer algorithms.[2][3]
The search experience takes into account varying sources of metadata, such as collaborative discovery of web pages, tags, social ranking, commenting on bookmarks, news, images, videos, knowledge sharing, podcasts and other web pages. Example forms of user input include social bookmarking or direct interaction with the search results such as promoting or demoting results the user feels are more or less relevant to their query.[4]
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The term social search began to emerge between 2004 and 2005. The concept of social ranking can be considered to derive from Google's PageRank algorithm, which assigns importance to web pages based on analysis of the link structure of the web, because PageRank is relying on the collective judgment of webmasters linking to other content on the web. Links, in essence, are positive votes by the webmaster community for their favorite sites.
In 2008, there were a few startup companies that focused on ranking search results according to one's social graph on social networks.[5][6] Companies in the social search space include folkd, Slangwho, Sproose, Mahalo, Jumper 2.0, Qitera, Scour, Wink, Eurekster, Baynote, Delver, OneRiot, and SideStripe. Former efforts include Wikia Search. In 2008, a story on TechCrunch showed Google potentially adding in a voting mechanism to search results similar to Digg's methodology.[7] This suggests growing interest in how social groups can influence and potentially enhance the ability of algorithms to find meaningful data for end users. There are also other services like Sentimnt that turn search personal by searching within the users' social circles.
The term Lazyweb has been used to describe the act of out-sourcing your questions to your friends, usually by broadcasting them on Twitter or Facebook (as opposed to posting them on Q&A websites such as Yahoo Answers). The company Aardvark, acquired by Google in February 2010, has created a more targeted version of this, which directs your questions to people in your social networks, based on relating the content of the question to the content of their social network pages. Aardvark users primarily use the Aardvark IM buddy, also integrated into Google Gmail, to ask and answer their questions. The company Cofacio released a beta platform in August 2009 in the UK which marks a return to the open, broadcast method of social search for the Twitter/Facebook generation.
In May 2011 Google rolled out its social search engine that was in beta since 2009.
To date social search engines have not demonstrated measurably improved search results over algorithmic search engines. However, there are potential benefits deriving from the human input qualities of social search.
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