SocialScope: Enabling Information Discovery on Social Content Sites

TL;DR

SocialScope presents a three-layer logical architecture for managing and discovering information on social content sites, integrating content and social data to enhance user experience and information analysis.

Contribution

It introduces a novel three-layer architecture and a uniform algebraic framework for effective information management and discovery on social content sites.

Findings

Proposes a three-layer architecture for social content sites

Develops a uniform algebraic framework for information tasks

Addresses challenges in content, discovery, and presentation layers

Abstract

Recently, many content sites have started encouraging their users to engage in social activities such as adding buddies on Yahoo! Travel and sharing articles with their friends on New York Times. This has led to the emergence of {\em social content sites}, which is being facilitated by initiatives like OpenID (http://www.openid.net/) and OpenSocial (http://www.opensocial.org/). These community standards enable the open access to users' social profiles and connections by individual content sites and are bringing content-oriented sites and social networking sites ever closer. The integration of content and social information raises new challenges for {\em information management and discovery} over such sites. We propose a logical architecture, named \kw{SocialScope}, consisting of three layers, for tackling the challenges. The {\em content management} layer is responsible for integrating, maintaining and physically accessing the content and social data. The {\em information discovery} layer takes care of analyzing content to derive interesting new information, and interpreting and processing the user's information need to identify relevant information. Finally, the {\em information presentation} layer explores the discovered information and helps users better understand it in a principled way. We describe the challenges in each layer and propose solutions for some of those challenges. In particular, we propose a uniform algebraic framework, which can be leveraged to uniformly and flexibly specify many of the information discovery and analysis tasks and provide the foundation for the optimization of those tasks.