Automatic Recognition of Text Difficulty from Consumers Health Information
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
Reliability prediction of webpages in the medical domain
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
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The ever-increasing volume of health online information, coupled with the uneven reliability and quality, may have considerable implications for the citizen. In order to address this issue, we propose to use, within a general or specialised search engine, standards for identifying the reliability of online documents. Standards used are those related to the ethics as well as trustworthiness of websites. In this research, they are detected through the URL names of Web pages by applying machine learning algorithms. According to algorithms used and to principles, our straightforward approach shows up to 93% precision and 91% recall. But a few principles remain difficult to recognize.