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We study how an online community perceives the relative quality of its own user-contributed content, which has important implications for the successful self-regulation and growth of the Social Web in the presence of increasing spam and a flood of Social Web metadata. We propose and evaluate a machine learning-based approach for ranking comments on the(More)
The explosion of the real-time web has spurred a growing need for new methods to organize, monitor, and distill relevant information from these large-scale social streams. One especially encouraging development is the self-curation of the real-time web via <i>user-driven linking</i>, in which users annotate their own status updates with lightweight semantic(More)
Large-scale socially-generated metadata is one of the key features driving the growth and success of the emerging Social Web. Recently there have been many research efforts to study the quality of this metadata that relies on quality assessments made by human experts external to a Social Web community. We are interested in studying how an online community(More)
With the growth in the past few years of social tagging services like Delicious and CiteULike, there is growing interest in modeling and mining these social systems for deriving implicit social collective intelligence. In this paper, we propose and explore two probabilistic generative models of the social annotation (or tagging) process with an emphasis on(More)
Group multicast is becoming a prevalent issue in network applications such as teleconferencing, pay-per-view, and information services. In order to secure group communications by providing confidentiality and trustworthiness of messages, various methods were proposed in the literature. In this paper, we compare some of these methods. In particular, we(More)
Social information systems - popularized by Facebook, Wikipedia, Twitter, and other social websites - are emerging as a powerful new paradigm for distributed social-powered information management. While there has been growing interest in these systems by businesses, government agencies, and universities, there remain important open challenges that must be(More)
Automatic forecasting of time series data is a challenging problem in many industries. Current forecast models adopted by businesses do not provide adequate means for including data representing external factors that may have a significant impact on the time series, such as weather, national events, local events, social media trends, promotions, etc. This(More)