Lu, Yingjie, et al. "Health-related hot topic detection in online communities using text clustering." Plos one 8.2 (2013): e56221.

This study addresses the challenge of identifying hot topics in online health communities by introducing an automatic topic detection method that integrates medical domain-specific features with traditional keyword-based text clustering. Testing the method on discussion boards for lung cancer, breast cancer, and diabetes, the research finds that common hot topics include symptoms, examinations, drugs, procedures, and complications, with notable differences in focus across disease types. This approach offers a scalable solution for understanding patient needs and interests in rapidly growing online health data.