Evaluating online health information: beyond readability formulas.
Although understanding health information is important, the texts provided are often difficult to understand. There are formulas to measure readability levels, but there is little understanding of how linguistic structures contribute to these difficulties. We are developing a toolkit of linguistic metrics that are validated with representative users and can be measured automatically. In this study, we provide an overview of our corpus and how readability differs by topic and source. We compare two documents for three groups of linguistic metrics. We report on a user study evaluating one of the differentiating metrics: the percentage of function words in a sentence. Our results show that this percentage correlates significantly with ease of understanding as indicated by users but not with the readability formula levels commonly used. Our study is the first to propose a user validated metric, different from readability formulas.
Leroy
Gondy
G
Claremont Graduate University, Claremont, CA, USA.
Helmreich
Stephen
S
Cowie
James R
JR
Miller
Trudi
T
Zheng
Wei
W
D000465:Algorithms; D001185:Artificial Intelligence; D003142:Communication; D032882:Comprehension; D054626:Consumer Health Information / Q000145:classification*; D020407:Internet; D009323:Natural Language Processing; D009862:Online Systems; D010363:Pattern Recognition, Automated / Q000379:methods*; D014481:United States