Social search

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. 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.

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.

History

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,^{[citation needed]} 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. Companies in the social search space include HeyStaks, Evam-SOCOTO Wajam, 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. 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 Sentiment 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 October 2009, Google rolled out its "Social Search" feature; after a time in beta, the feature was expanded to multiple languages in May 2011. However, after a search deal with Twitter ended without renewal, Google began to retool its Social Search. In January 2012, Google released "Search plus Your World", a further development of Social Search. The feature, which is integrated into Google's regular search as an opt-out feature, pulls references to results from Google+ profiles. The company was subsequently criticized by Twitter for the perceived potential impact of "Search plus Your World" upon web publishers, describing the feature's release to the public as a "bad day for the web", while Google replied that Twitter refused to allow deep search crawling by Google of Twitter's content.

Benefits

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.

• Reduced impact of link spam by relying less on link structure of web pages.
• Increased relevance because each result has been selected by users.
• Leverage a network of trusted individuals by providing an indication of whether they thought a particular result was good or bad.
• The introduction of 'human judgement' suggests that each web page has been viewed and endorsed by one or more people, and they have concluded it is relevant and worthy of being shared with others using human techniques that go beyond the computer's current ability to analyze a web page.
• Web pages are considered to be relevant from the reader's perspective, rather than the author who desires their content to be viewed, or the web master as they create links.
• More current results. Because a social search engine is constantly getting feedback it is potentially able to display results that are more current or in context with changing information.

Concerns

• Risk of spam. Because users can directly add results to a social search engine there is a risk that some users could insert search spam directly into the search engine. Elimination or prevention of this spam would require the ability to detect the validity of a user's' contribution, such as whether it agrees with other trusted users.
• "The Long Tail" of search is a concept that there are so many unique searches conducted that most searches, while valid, are performed very infrequently. A search engine that relied on users filling in all the searches would be at a disadvantage to one that used machines to crawl and index the entire web.

See also

• Collaborative filtering
• Enterprise bookmarking
• Human search engine
• Relevance feedback
• Social software