Social search
Social search is a behavior of retrieving and searching on a social searching engine that mainly searches
Social search may not be demonstrably better than algorithm-driven search.
Research and implementations
Over the years, there have been different studies, researches and some implementations of Social Search. In 2008, there were a few startup companies that focused on ranking search results according to one's
In 2009, a startup project called HeyStaks (www.heystaks.com) developed a web browser plugin "HayStaks". HeyStaks applies social search through collaboration in web search as a way that leads to better search results.[15] The main motivation for HeyStaks to work on this idea is to provide the user with features that search engines didn't provide at that time. For instance, different searches have indicated that about 70% of the time when user search for something, a friend or a coworker have found it already. Also, studies have shown that approximately, 30% of people who use online search, search for something that they have found before.[16] The startup believe that they help avoid these kind of issues by providing a shared and rich search experience through a list of recommendations that get generated based on search results.
In October 2009,
In December 2008, Twitter had re-introduced their people search feature.[21] While the interface had since changed significantly, it allows you to search either full names or usernames in a straight-forward search engine.
In January 2013,
Although there have been different researches and studies in social search, social media networks have not vested enough interest in working with
Social discovery
Social discovery is the use of social preferences and personal information to predict what content will be desirable to the user.
Social search engines
A social search engine in an aspect can be thought of as a search engine that provides an answer for a question from another answer by identifying a person in the answer. That can happen by retrieving a user submitted query and determining that the query is related to the question; and provides an answer, including the link to the resource, as part of search results that are responsive to the query.[28]
Few social search engines depend only on online communities. Depending on the feature-set of a particular search engine, these results may then be saved and added to community search results, further improving the relevance of results for future searches of that keyword. Social search engines are considered a part of Web 2.0 because they use the collective filtering of online communities to elevate particularly interesting or relevant content using tagging. These descriptive tags add to the meta data embedded in Web pages, theoretically improving the results for particular keywords over time. A user will generally see suggested tags for a particular search term, indicating tags that have previously been added.
An implementation of a social search engine is Aardvark. Aardvark is a social search engine that is based on the "village paradigm" which is about connecting the user who has a question with friends or friends of friends whom can answer his or her question.[29] In Aadvark, a user ask a question in different ways that mostly involves online ways such as instant messaging, email, web input or other non-online ways such as text message or voice. The Aardvark algorithm forwards the question to someone in the asker extended social network who has the highest probability in knowing the answer to the question. Aadvark was obtained by Google in 2010 and abandoned later in 2011.
Potential drawbacks to social search lie in its open structure, as is the case with other tagged databases. As these are trust-based networks, unintentional or malicious misuse of tags in this context can lead to imprecise search results. There are number of social search engines that mainly based on tracking user information to order to provide related search results. Examples of this types are
.Developments
Confirmed to be in testing, a new Facebook app feature called 'Add a Link' lets users see popular articles they might want to include in their status updates and comments by entering a search query. The results appear to comprise articles that have been well-shared by other Facebook users, with the most recently published given priority over others. The option certainly makes it easier for users to add links without manually searching their News Feed or resorting to a Google query. This new app reduce users' reliance on Google Search.[32]
Twitter announced it is replacing its 'Discover' tab with 'Tailored Trends'. The new Tailored Trends feature, besides showing Twitter trends, will give a short description of each topic. Since trends tend to be abbreviations without context, a description will make it more clear what a trend is about. The new trends experience may also include how many Tweets have been sent and whether a topic is trending up or down.[33][34]
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.[35]
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
Despite the advantages of
Another issue related to both distributed and centralized search is how to more accurately understand user intent from observed multimedia data. The solutions are based on how to effectively and efficiently leverage social media and search engine. A potential method is to derive a user-image interest graph from social media, and then re-rank image search results by integrating social relevance from the user-image interest graph and visual relevance from general search engines.[39][40]
Besides above engineering explorations, a more fundamental and potential method is to develop social search systems based on the understanding of related neural mechanisms. Search problems scale from individuals to societies, however, recent trends across disciplines indicate that the formal properties of these problems share similar structures and, often, similar solutions. Moreover, internal search (e.g., memory search) shows similar characteristics to external search (e.g., spatial foraging), including shared neural mechanisms consistent with a common evolutionary origin across species. For search scenarios, organisms must detect – and climb – noisy, long-range environmental (e.g., temperature, salinity, resource) gradients. Here,
See also
- Social Computing
- Social Navigation
- Online community
- Web Community
- Collaborative filtering
- Collaborative information seeking
- Enterprise bookmarking
- Human search engine
- Relevance feedback
- Social information seeking
- Social software
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- ^ New Sites Make It Easier To Spy on Your Friends, Wall Street Journal, May 13. 2008
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- ^ Is This The Future Of Search?, TechCrunch, July 16, 2008
- ^ Barry Smyth, Peter Briggs, Maurice Coyle, and Michael O’Mahony (2009). Google Shared. A Case-Study in Social Search
- ^ Smyth, B., Balfe, E., Freyne, J., Briggs, P., Coyle, M., Boydell, O.: Exploiting query repetition and regularity in an adaptive community-based web search engine. User Model. User-Adapt. Interact. 14(5), 383–423 (2004)
- ^ "Retweets and Likes influencing search results". March Communications. 10 April 2013. Archived from the original on 18 March 2014. Retrieved 1 December 2014.
- ^ a b "Facebook Announces New Social Search Feature". HubSpot. 15 January 2013. Retrieved 1 December 2014.
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- ^ a b "Google pushing Google+". Third Door Media. 18 November 2014. Retrieved 1 December 2012.
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- ^ Burke, Amy (8 July 2013). "Are Social Discovery Apps Too Creepy?". Mashable.
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- ^ Constine, Josh (10 September 2013). "Bitcovery Brings A Desperately Needed Social Discovery Layer To The iTunes Store". TechCrunch.
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- ^ Damon Horowitz, Sepandar D. Kamvar(April 1020) The Anatomy of a Large-Scale Social Search Engine
- ^ "Social Searcher - Social Media Search Engine"
- ^ About Social Searcher, accessed 24 March 2023
- ^ Constine, Josh (May 9, 2015). "Skip Googling With Facebook's New "Add A Link" Mobile Status Search Engine". Techcrunch.
- ^ Cselle, Gabor (April 8, 2015). "Updating trends on mobile". Twitter.
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