The proliferation of social media has the potential for changing the structure and organization of the web. In the past, scientists have looked at the web as a large connected component to understand how the topology of hyperlinks correlates with the quality of information contained in the page and they proposed techniques to rank information contained in web pages. We argue that information from web pages and network data on social relationships can be combined to create a personalized and socially connected web. In this paper, we look at the web as a composition of two networks, one consisting of information in web pages and the other of personal data shared on social media web sites. Together, they allow us to analyze how social media tunnels the flow of information from person to person and how to use the structure of the social network to rank, deliver, and organize information specifically for each individual user. We validate our social ranking concepts through a ranking experiment conducted on web pages that users shared on Google Buzz and Twitter.
While social searches have been introduced in multiple settings from the Social Query Model (SQM) @cite_0 to the implementation of social searching applications for mobile devices @cite_11 , a good amount of work has focused on finding the right answer to a search query by routing the search query to the right person in a social network graph @cite_5 @cite_11 . We studied the structure of the network to socially and automatically rank URLs without users intervention. In the Social Query Model @cite_0 , routing paths of search queries are studied in decentralized systems where indeterministic behavior of each agent willing to provide a correct answer with some level of accuracy and expertise is taken into consideration when forming an optimal routing policy. In Aardvark @cite_11 , the focus was to route a query from the searcher to a designated user in a social network that was assumed to be able to provide an answer. We took the approach of using network flow where the goal is to automatically rank a set of pages through the eyes of the searcher's social ties.
@cite_0 @cite_5 @cite_11
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Decentralized search by routing queries over a network is fast emerging as an important research problem, with potential applications in social search as well as peer-to-peer networks [17, 18]. In this paper, we introduce a novel Social Query Model (SQM) for decentralized search, which factors in realistic elements such as expertise levels and response rates of nodes, and has the Pagerank model and certain Markov Decision Processes as special cases. In the context of the model, we establish the existence of a query routing policy that is simultaneously optimal for all nodes, in that no subset of nodes will jointly have any incentive to use a different local routing policy. For computing the optimal policy, we present an efficient distributed approximation algorithm that is almost linear in the number of edges in the network. Extensive experiments on both simulated random graphs and real small-world networks demonstrate the potential of our model and the effectiveness of the proposed routing algorithm. The growth of Web 2.0 and fundamental theoretical breakthroughs have led to an avalanche of interest in social networks. This paper focuses on the problem of modeling how social networks accomplish tasks through peer production style collaboration. We propose a general interaction model for the underlying social networks and then a specific model ( i L ink for social search and message routing. A key contribution here is the development of a general learning framework for making such online peer production systems work at scale. The i L ink model has been used to develop a system for FAQ generation in a social network (FAQ tory ), and experience with its application in the context of a full-scale learning-driven workflow application (CALO) is reported. We also discuss methods of adapting i L ink technology for use in military knowledge sharing portals and other message routing systems. Finally, the paper shows the connection of i L ink to SQM, a theoretical model for social search that is a generalization of Markov Decision Processes and the popular Pagerank model. We present Aardvark, a social search engine. With Aardvark, users ask a question, either by instant message, email, web input, text message, or voice. Aardvark then routes the question to the person in the user's extended social network most likely to be able to answer that question. As compared to a traditional web search engine, where the challenge lies in finding the right document to satisfy a user's information need, the challenge in a social search engine like Aardvark lies in finding the right person to satisfy a user's information need. Further, while trust in a traditional search engine is based on authority, in a social search engine like Aardvark, trust is based on intimacy. We describe how these considerations inform the architecture, algorithms, and user interface of Aardvark, and how they are reflected in the behavior of Aardvark users.