TY - GEN
T1 - Reliability prediction of webpages in the medical domain
AU - Sondhi, Parikshit
AU - Vinod Vydiswaran, V. G.
AU - Zhai, Chengxiang
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84860148507&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84860148507&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-28997-2_19
DO - 10.1007/978-3-642-28997-2_19
M3 - Conference contribution
AN - SCOPUS:84860148507
SN - 9783642289965
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 219
EP - 231
BT - Advances in Information Retrieval - 34th European Conference on IR Research, ECIR 2012, Proceedings
T2 - 34th European Conference on Information Retrieval, ECIR 2012
Y2 - 1 April 2012 through 5 April 2012
ER -