Predicting Consumer Familiarity with Health Topics by Query Formulation and Search Result Interaction

Abstract

Searching for understandable health information on the Internet remains difficult for most consumers. Every consumer has different health topic familiarity. This diversity may cause misunderstanding because the information presented during health information searches may not fit the consumer’s understanding. This study aimed to develop health topic familiarity prediction models based on the consumer’s searching behavior, how the consumers formulate the query and how they interact with the search results. The experimental results show that Naïve Bayes and Sequential Minimal Optimization classifiers achieved high accuracy on the combination of query formulation and search result interaction feature sets in predicting consumer’s health topic familiarity. This finding suggests that health topic familiarity identification based on the query formulation and the search result interaction is feasible and effective.