The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
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
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
Automatically characterizing resource quality for educational digital libraries
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
Revisiting readability: a unified framework for predicting text quality
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Aspects of 'relevance' in the alignment of curriculum with educational standards
Information Processing and Management: an International Journal
Learning to predict readability using diverse linguistic features
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Computer-assisted assignment of educational standards using natural language processing
Journal of the American Society for Information Science and Technology
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Efficient learning from Web resources can depend on accurately assessing the quality of each resource. We present a methodology for developing computational models of quality that can assist users in assessing Web resources. The methodology consists of four steps: 1) a meta-analysis of previous studies to decompose quality into high-level dimensions and low-level indicators, 2) an expert study to identify the key low-level indicators of quality in the target domain, 3) human annotation to provide a collection of example resources where the presence or absence of quality indicators has been tagged, and 4) training of a machine learning model to predict quality indicators based on content and link features of Web resources. We find that quality is a multifaceted construct, with different aspects that may be important to different users at different times. We show that machine learning models can predict this multifaceted nature of quality, both in the context of aiding curators as they evaluate resources submitted to digital libraries, and in the context of aiding teachers as they develop online educational resources. Finally, we demonstrate how computational models of quality can be provided as a service, and embedded into applications such as Web search.