Class-based n-gram models of natural language
Computational Linguistics
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
IEEE Transactions on Knowledge and Data Engineering
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Proceedings of the 16th international conference on World Wide Web
Truth Discovery with Multiple Conflicting Information Providers on the Web
IEEE Transactions on Knowledge and Data Engineering
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Harnessing the wisdom of crowds in wikipedia: quality through coordination
Proceedings of the 2008 ACM conference on Computer supported cooperative work
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Open information extraction from the web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A framework for entailed relation recognition
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Wisdom of crowds versus wisdom of linguists – measuring the semantic relatedness of words
Natural Language Engineering
Content-driven trust propagation framework
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Trust ranking of medical websites
Proceedings of the 4th ACM conference on Data and application security and privacy
Hi-index | 0.00 |
As more and more content is published and consumed online, it is imperative to know if an information nugget found on the Web is trustworthy or not. This is especially important for online medical information as it affects the most vulnerable group of users looking for medical help online. In this paper, we study the feasibility of automatically assessing the trustworthiness of a medical claim based on community knowledge, and propose techniques to assign a reliability score for an information nugget based on support over a community-generated collection. Specifically, we model the trustworthiness of a medical claim based on experiences shared by users in health forums and mailing lists. The proposed claim scores can be used to rank related claims on their relative trustworthiness. We further extend the notion of trustworthiness to a site (or equivalently, a database of claims from the site) and propose a scheme to rank sites based on aggregating the trust scores of claims from the site. Our experiments show that community knowledge can be exploited to help users distinguish reliable medical claims from unreliable ones. The proposed techniques can be applied to other domains where similar corpora are available.