Social search

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Definition and Overview of Social Search
– Social search is a behavior of retrieving and searching on a social searching engine that mainly searches user-generated content such as news, videos, and images related to search queries on social media platforms.
– It combines traditional algorithms with the idea of taking into account social relationships between search results and the searcher.
– Social search is a personalized search technology that uses online community filtering to produce highly personalized results.
– It aims to provide more meaningful and relevant results by leveraging human network-oriented results instead of relying solely on computer algorithms.
– Social search can be performed on various platforms such as search engines, social media platforms, and specialized social search engines.
– It utilizes social signals such as likes, shares, and comments to influence search rankings.
– The goal of social search is to enhance the search experience by leveraging social interactions and recommendations.

Benefits and Limitations of Social Search
– Social search is not demonstrably better than algorithm-driven search.
– It highlights content that was created or touched by other users in the social graph of the person conducting the search.
– It allows for a shared and rich search experience through recommendations generated based on search results.
– It can improve the relevance of results for future searches of a particular keyword.
– It provides a more personalized search experience by considering social relationships and connections.
– Privacy concerns arise when personal information is used for social search.
– The reliability and credibility of user-generated content can be a challenge in social search.
– The diversity of social networks and preferences can make it difficult to provide personalized search results.
– Social search algorithms need to constantly adapt to changing social dynamics and user behavior.
– The balance between personalized search results and maintaining diversity in search results is a challenge.

Research and Implementations of Social Search
– Various startup companies 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.
Google introduced Social Search in 2009, which was later expanded to multiple languages.
– Bing and Google started considering re-tweets and Likes when providing search results.
– HeyStaks developed a web browser plugin that applies social search through collaboration in web search to improve search results.

Social Discovery
– Social discovery uses social preferences and personal information to predict desirable content for users.
– It enables the discovery of new people, experiences, shopping, and traveling.
– Real-time social discovery is facilitated by mobile apps.
– Social discovery contributes to sales and revenue for companies through social media.
– Facebook’s profitability is based on social discovery, generating ad revenue by targeting ads to users using their social connections.

Social Search Engines
– A social search engine provides answers to questions by identifying a person in the answer.
– It retrieves user-submitted queries related to the question and provides an answer with a link to the resource.
– Social search engines leverage user-generated content and social connections to enhance search results.
– They can be used to discover new people, experiences, and products.
– Social search engines contribute to the profitability of platforms like Facebook by targeting ads based on social connections.

Social search (Wikipedia)

Social search is a behavior of retrieving and searching on a social searching engine that mainly searches user-generated content such as news, videos and images related search queries on social media like Facebook, LinkedIn, Twitter, Instagram and Flickr. It is an enhanced version of web search that combines traditional algorithms. The idea behind social search is that instead of ranking search results purely based on semantic relevance between a query and the results, a social search system also takes into account social relationships between the results and the searcher. The social relationships could be in various forms. For example, in LinkedIn people search engine, the social relationships include social connections between searcher and each result, whether or not they are in the same industries, work for the same companies, belong the same social groups, and go the same schools, etc.

Social search may not be demonstrably better than algorithm-driven search. In the algorithmic ranking model that search engines used in the past, relevance of a site is determined after analyzing the text and content on the page and link structure of the document. In contrast, search results with social search highlight content that was created or touched by other users who are in the Social Graph of the person conducting a search. It is a personalized search technology with online community filtering to produce highly personalized results. 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. 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. The principle behind social search is that human network oriented results would be more meaningful and relevant for the user, instead of computer algorithms deciding the results for specific queries.

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