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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To answer search queries on a social network rich with user-generated content, it is desirable to give a higher ranking to content that is closer to the individual issuing the query. Queries occur at nodes in the network, documents are also created by nodes in the same network, and the goal is to find the document that matches the query and is closest in network distance to the node issuing the query. In this paper, we present the "Partitioned Multi-Indexing" scheme, which provides an approximate solution to this problem. With m links in the network, after an offline O(m) pre-processing time, our scheme allows for social index operations (i.e., social search queries, as well as insertion and deletion of words into and from a document at any node), all in time O(1). Further, our scheme can be implemented on open source distributed streaming systems such as Yahoo! S4 or Twitter's Storm so that every social index operation takes O(1) processing time and network queries in the worst case, and just two network queries in the common case where the reverse index corresponding to the query keyword is much smaller than the memory available at any distributed compute node. Building on Das 's approximate distance oracle, the worst-case approximation ratio of our scheme is O(1) for undirected networks. Our simulations on the social network Twitter as well as synthetic networks show that in practice, the approximation ratio is actually close to 1 for both directed and undirected networks. We believe that this work is the first demonstration of the feasibility of social search with real-time text updates at large scales. 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. In this paper, we present the results of a user study on exploratory search activities in a social science digital library. We conducted a user study with 32 participants with a social sciences background—16 postdoctoral researchers and 16 students—who were asked to solve a task on searching related work to a given topic. The exploratory search task was performed in a 10-min time slot. The use of certain search activities is measured and compared to gaze data recorded with an eye tracking device. We use a novel tree graph representation to visualise the users’ search patterns and introduce a way to combine multiple search session trees. The tree graph representation is capable of creating one single tree for multiple users and identifying common search patterns. In addition, the information behaviour of students and postdoctoral researchers is being compared. The results show that search activities on the stratagem level are frequently utilised by both user groups. The most heavily used search activities were keyword search, followed by browsing through references and citations, and author searching. The eye tracking results showed an intense examination of documents metadata, especially on the level of citations and references. When comparing the group of students and postdoctoral researchers, we found significant differences regarding gaze data on the area of the journal name of the seed document. In general, we found a tendency of the postdoctoral researchers to examine the metadata records more intensively with regard to dwell time and the number of fixations. By creating combined session trees and deriving subtrees from those, we were able to identify common patterns like economic (explorative) and exhaustive (navigational) behaviour. Our results show that participants utilised multiple search strategies starting from the seed document, which means that they examined different paths to find related publications. Models of human navigation play an important role for understanding and facilitating user behavior in hypertext systems. In this paper, we conduct a series of principled experiments with decentralized search - an established model of human navigation in social networks - and study its applicability to information networks. We apply several variations of decentralized search to model human navigation in information networks and we evaluate the outcome in a series of experiments. In these experiments, we study the validity of decentralized search by comparing it with human navigational paths from an actual information network - Wikipedia. We find that (i) navigation in social networks appears to differ from human navigation in information networks in interesting ways and (ii) in order to apply decentralized search to information networks, stochastic adaptations are required. Our work illuminates a way towards using decentralized search as a valid model for human navigation in information networks in future work. Our results are relevant for scientists who are interested in modeling human behavior in information networks and for engineers who are interested in using models and simulations of human behavior to improve on structural or user interface aspects of hypertextual systems.