Search engine researchers typically depict search as the solitary activity of an individual searcher. In contrast, results from our critical-incident survey of 150 users on Amazon's Mechanical Turk service suggest that social interactions play an important role throughout the search process. Our main contribution is that we have integrated models from previous work in sensemaking and information seeking behavior to present a canonical social model of user activities before, during, and after search, suggesting where in the search process even implicitly shared information may be valuable to individual searchers.
However, in addition to explicit collaboration in joint search tasks @cite_9 , we believe that even implicit social experiences could improve the search process. Therefore, the general term social search'' may more suitably describe information seeking and sensemaking habits that make use of a range of possible social interactions: including searches that utilize social and expertise networks or that may be done in shared social workspaces. This notion certainly encompasses collaborative co-located search, as well as remote and asynchronous collaborative and collective search. Our focus in this paper is to explore a model of social search that may offer suggestions for supporting social interactions in the information seeking process.
@cite_9
2022555286
Search engine researchers typically depict search as the solitary activity of an individual searcher. In contrast, results from our critical-incident survey of 150 users on Amazon's Mechanical Turk service suggest that social interactions play an important role throughout the search process. Our main contribution is that we have integrated models from previous work in sensemaking and information seeking behavior to present a canonical social model of user activities before, during, and after search, suggesting where in the search process both explicitly and implicitly shared information may be valuable to individual searchers. Studies of search habits reveal that people engage in many search tasks involving collaboration with others, such as travel planning, organizing social events, or working on a homework assignment. However, current Web search tools are designed for a single user, working alone. We introduce SearchTogether, a prototype that enables groups of remote users to synchronously or asynchronously collaborate when searching the Web. We describe an example usage scenario, and discuss the ways SearchTogether facilitates collaboration by supporting awareness, division of labor, and persistence. We then discuss the findings of our evaluation of SearchTogether, analyzing which aspects of its design enabled successful collaboration among study participants. Search is not just a box and ten blue links. Search is a journey: an exploration where what we encounter along the way changes what we seek. But in order to guide people along this journey, designers must understand both the art and science of search.In Designing the Search Experience, authors Tony Russell-Rose and Tyler Tate weave together the theories of information seeking with the practice of user interface design. Understand how people search, and how the concepts of information seeking, information foraging, and sensemaking underpin the search process. Apply the principles of user-centered design to the search box, search results, faceted navigation, mobile interfaces, social search, and much more. Design the cross-channel search experiences of tomorrow that span desktop, tablet, mobile, and other devices. Table of Contents PART 1 A Framework for Search and Discovery Chapter 1 The User Chapter 2 Information Seeking Chapter 3 Context Chapter 4 Modes of Search and Discovery PART 2 Design Solutions Chapter 5 Formulating the Query Chapter 6 Displaying and Manipulating Results Chapter 7 Faceted Search Chapter 8 Mobile Search Chapter 9 Social Search Part 3 Designing the Future Chapter 10 Cross-Channel Information Interaction 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.