Abstract
With the prevalence of online healthcare communities (OHCs), increasingly more people are seeking health-related information in OHCs. However, the large amount of health-related knowledge of varying quality poses a challenge for people to quickly find truly helpful knowledge. This study proposes a framework for automatically identifying helpful health-related knowledge based on a knowledge adoption model and machine learning techniques. Extensive experiments on the dataset from one of China's largest OHCs have demonstrated the superiority of our framework. This study strengthens the understanding of readers’ value judgments of online health-related knowledge and enriches research in information systems and knowledge management.
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