Paper: Collecting Community Wisdom: Integrating Social Search & Social Navigation

Authors:

The goal of the paper is to detail the integration of social search and social navigation and highlight the benefits of integration on two levels. The paper argues that both technologies harvest and harness “community wisdom ”. The integration can give more benefit from the additional community wisdom gathered by the community when assisting users to locate relevant information. This service allows users to effectively combine their search and browsing behaviors.


At first, the paper introduces information growing as making navigation through the ever-changing information become increasingly difficult.Some research efforts have highlighted the interactive nature of information access behavior and promoted the potential value of harnessing user activity patterns to drive the next generation of social information access tools. Another research on social search has highlighted how the search patterns (queries and selection) of users and communities of users can be used when responding to future searches. It considered adaptation of a result-list to the needs and preferences of a particular community. The authors believe that those researches suffer from a lack of integration.Each approach encapsulates within its own information access paradigms.Each gathers its own collection of community wisdom which is never shared with other approaches.

Basically example, a student working for paper social navigation participates in class on advanced information access. The class forms a community of interest. → Students have same interest in social navigation. When other students search and browse information, all action i.e queries, comments, tags, and annotations will be combined to express the community wisdom of the class. The important of these relevance information is following:

• When a student found an article, he may don’t know the important of this article.

• That community wisdom can guide this result with some indication.

The paper then talk about Social Browsing and Social Search. Social Browsing can be considered as an application of the general idea of social navigation, applied to the specific context of hypertext link browsing. On Social Search, modern search engine continue to struggle when it comes to delivering the right results to the right users and the right time. One core problem that developers have little control over concerns the average searcher’s inability to express their search needs effectively in the form of a query. Typical search queries are vague, unclear, or under specified when it comes to revealing a searcher’s true information needs.

They performed the experiment on Integrated Social Information Access. The system combined social search component based on I-SPY and social navigation component based on AnnotatEd, used in Knowledge Sear II. The integration was based on two critical ideas.

• The community wisdom collected by the search component should also be used to support usage of the navigation component.
• Social search and social browsing should be integrated at the interface level. The user should be able to move from search to browsing without losing the context of work and community-based support.

The result of evaluation shows that all subjects indicated that they noticed the social cues and they were often pulled by social cues. All subjects responded that the social annotations are useful in general and the presence of social icons made it easier to locate information of an article.

• On the level of the internal mechanisms, the integration of two social technologies was achieved by taking into account the “community wisdom” collected by both browsing and search components when supporting the users work with either of the components.
• On the interface level, this integration allowed the users to use both ways of information access without loosing the context of work and social navigation support.
• Social cues informed the users about the relevance of all articles discovered on the way to similar past search.
• The visible list of queries was used to highlight useful queries that could be immediately executed by simply clicking on a query link.

Thank you the authors for providing the guidance for implementing social browsing with social search component.
The paper can be found here