The nature of statistical learning theory
The nature of statistical learning theory
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
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Link analysis ranking: algorithms, theory, and experiments
ACM Transactions on Internet Technology (TOIT)
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Quality and relevance of domain-specific search: A case study in mental health
Information Retrieval
Reliability and verification of natural language text on the world wide web
Reliability and verification of natural language text on the world wide web
Exploring both Content and Link Quality for Anti-Spamming
CIT '06 Proceedings of the Sixth IEEE International Conference on Computer and Information Technology
Link analysis for Web spam detection
ACM Transactions on the Web (TWEB)
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
Robust PageRank and locally computable spam detection features
AIRWeb '08 Proceedings of the 4th international workshop on Adversarial information retrieval on the web
Content-driven trust propagation framework
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
EmSe: initial evaluation of a child-friendly medical search system
Proceedings of the 4th Information Interaction in Context Symposium
Detecting Fake Medical Web Sites Using Recursive Trust Labeling
ACM Transactions on Information Systems (TOIS)
Web credibility: features exploration and credibility prediction
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
CredibleWeb: a platform for web credibility evaluation
CHI '13 Extended Abstracts on Human Factors in Computing Systems
Leveraging conceptual lexicon: query disambiguation using proximity information for patent retrieval
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
On the subjectivity and bias of web content credibility evaluations
Proceedings of the 22nd international conference on World Wide Web companion
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In this paper, we study how to automatically predict reliability of web pages in the medical domain. Assessing reliability of online medical information is especially critical as it may potentially influence vulnerable patients seeking help online. Unfortunately, there are no automated systems currently available that can classify a medical webpage as being reliable, while manual assessment cannot scale up to process the large number of medical pages on the Web. We propose a supervised learning approach to automatically predict reliability of medical webpages. We developed a gold standard dataset using the standard reliability criteria defined by the Health on Net Foundation and systematically experimented with different link and content based feature sets. Our experiments show promising results with prediction accuracies of over 80%. We also show that our proposed prediction method is useful in applications such as reliability-based re-ranking and automatic website accreditation.