The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Making large-scale support vector machine learning practical
Advances in kernel methods
Incorporating quality metrics in centralized/distributed information retrieval on the World Wide Web
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Empirically validated web page design metrics
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
What makes Web sites credible?: a report on a large quantitative study
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Judgement of information quality and cognitive authority in the Web
Journal of the American Society for Information Science and Technology
Understanding educator perceptions of "quality" in digital libraries
Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries
Untangling compound documents on the web
Proceedings of the fourteenth ACM conference on Hypertext and hypermedia
Digital libraries and educational practice: a case for new models
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Automatic evaluation of aspects of document quality
Proceedings of the 22nd annual international conference on Design of communication: The engineering of quality documentation
A content-driven reputation system for the wikipedia
Proceedings of the 16th international conference on World Wide Web
As we may perceive: finding the boundaries of compound documents on the web
Proceedings of the 17th international conference on World Wide Web
Size matters: word count as a measure of quality on wikipedia
Proceedings of the 17th international conference on World Wide Web
Computing trust from revision history
Proceedings of the 2006 International Conference on Privacy, Security and Trust: Bridge the Gap Between PST Technologies and Business Services
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With the rise of community-generated web content, the need for automatic assessment of resource quality has grown. We demonstrate how developing a concrete characterization of quality for web-based resources can make machine learning approaches to automating quality assessment in the realm of educational digital libraries tractable. Using data from several previous studies of quality, we gathered a set of key dimensions and indicators of quality that were commonly identified by educators. We then performed a mixed-method study of digital library quality experts, showing that our characterization of quality captured the subjective processes used by the experts when assessing resource quality. Using key indicators of quality selected from a statistical analysis of our expert study data, we developed a set of annotation guidelines and annotated a corpus of 1000 digital resources for the presence or absence of the key quality indicators. Agreement among annotators was high, and initial machine learning models trained from this corpus were able to identify some indicators of quality with as much as an 18% improvement over the baseline.