A balanced approach to health information evaluation: A vocabulary-based naïve Bayes classifier and readability formulas

TL;DR: A vocabularly-based, naive Bayes classifier to distinguish between three difficulty levels in text is developed, indicating that vocabulary usage is frequently appropriate in text considered too difficult by readability formula evaluations.

Abstract: Since millions seek health information online, it is vital for this information to be comprehensible. Most studies use readability formulas, which ignore vocabulary, and conclude that online health information is too difficult. We developed a vocabularly-based, naive Bayes classifier to distinguish between three difficulty levels in text. It proved 98% accurate in a 250-document evaluation. We compared our classifier with readability formulas for 90 new documents with different origins and asked representative human evaluators, an expert and a consumer, to judge each document. Average readability grade levels for educational and commercial pages was 10th grade or higher, too difficult according to current literature. In contrast, the classifier showed that 70-90% of these pages were written at an intermediate, appropriate level indicating that vocabulary usage is frequently appropriate in text considered too difficult by readability formula evaluations. The expert considered the pages more difficult for a consumer than the consumer did. © 2008 Wiley Periodicals, Inc.