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 searches the Social-Graph uses 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.
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. Companies in the social search space include Evam-SOCOTO Wajam, Slangwho, Sproose, Mahalo, Jumper 2.0, Qitera, Scour, Wink, Eurekster, Baynote, Delver, and OneRiot. 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.
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. Before the expansion however in 2010 Bing and Google were already taking into account re-tweets and Likes when providing search results. 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 goal was to deliver better, more relevant and personalized search results with this integration. This integration however had some problems in which Google+ still isn't wildly adopted or has much usage among many users.
In January 2013, Facebook announced a new search engine called Graph Search still in the beta stages. The goal in mind was to accomplish what Google failed at, skipping the results that are popular to the internet, in favor of the results that are popular within your social circle. Unlike Google, Facebook's Graph search differed in two large areas, first, people use Facebook frequently. This allows Facebook to use all its user generated content that is uploaded everyday to improve the Facebook search experience. Secondly, Facebook did not incorporate Google into Facebook search, instead Graph Search is powered by Bing. This allows Bing results to show when Facebook's Graph Search can't find a match.
When Google announced "Search plus Your World" the reaction was mixed among tech companies. 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. The criticism from Twitter wasn't without merits however, by Google integrating Google+, they were essentially forcing people to switch from a social network on to theirs in order to improve search results. One famous example occurred when Google showed a link to Mark Zuckerberg's dormant Google+ account rather than the active Facebook profile. Further more this affected businesses in which if they do not have time to leverage all other social media sites, they knew they should use Google+ to maximize their efforts since the data shows it impacts rankings more than Twitter and Facebook. In November 2014 these accusations started to die down because Google's Knowledge Graph started to finally show links to Facebook, Twitter, and other social media sites.
Google was not the only one that garnished concerns over social search. After the introduction of Graph Search by Facebook many pointed out how Graph Search showed private information that isn't in web search. Information that was once obscure is now easier to dig up, which is why Facebook urges users to monitor post and pictures users are tagged in and filter and filter any content that users would not want to make public.
This in large points towards the biggest concern toward social search which is that social media networks don't have a vested interest in working with search engines. LinkedIn for example has taken steps to improve its own individual search functions in order to stray users from external search engines. Even Microsoft started working with Twitter in order to integrate some tweets into Bing's search results in November 2013. Yet Twitter has its own search engine which points out how much value their data has and why they'd like to keep it in house. In the end though social search will never be truly comprehensive of the subjects that matter to people unless users opt to be completely public with their information.
Social discovery is the use of social preferences and personal information to predict what content will be desirable to the user. Technology is used to discover new people and sometimes new experiences shopping, meeting friends or even traveling. The discovery of new people is often in real-time, enabled by mobile apps. However, social discovery is not limited to meeting people in real-time, it also leads to sales and revenue for companies via social media. An example of retail would be the addition of social sharing with music, through the iTunes music store. There is a social component to discovering new music  Social discovery is at the basis of Facebook's profitability, generating ad revenue by targeting the ads to users using the social connections to enhance the commercial appeal.
Google may be falling behind in terms of social search, but in reality they see the potential and importance of this technology with Web 3.0 and web semantics. The importance of social media lies within how Semantic search works. Semantic search understands much more, including where you are, the time of day, your past history, and many other factors including social connections, and social signals. The first step in order to achieve this will be to teach algorithms to understand the relationship between things.
However this is not possible unless social media sites decide to work with search engines, which is difficult since everyone would like to be the main toll bridge to the internet. As we continue on, and more articles are referred by social media sites, the main concern becomes what good is a search engine without the data of users.
One development that seeks to redefine search is the combination of distributed search with social search. The goal is a basic search service whose operation is controlled and maintained by the community itself. This would largely work like Peer to Peer networks in which users provide the data they seems appropriate. Since the data used by search engines belongs to the user they should have absolute control over it. The infrastructure required for a search engine is already available in the from of thousands of idle desktops and extensive residential broadband access.
- Collaborative filtering
- Enterprise bookmarking
- Human search engine
- Relevance feedback
- Social software
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