Analysis of user trends in digital health communities using big data mining

Abstract

Camoni, the largest digital health community in Israel, involves thousands of patients in the decision-making process concerning their illness and treatment. This approach reflects the recent global shift towards digital tools that combine professional information with social networking capabilities to enable problem-solving, emotional support, and knowledge sharing. Digital health communities serve as an invaluable resource for individuals seeking to learn more about their health, connect with others with shared experiences, and receive encouragement. Our research investigates user trends in digital health communities using the Camoni platform as a case study. To this end, we compile a comprehensive database of 12 years of site activity and conduct a large-scale analysis to identify and assess significant trends in user behavior. We observe several significant trends concerning different genders engagement and note a narrowing of gaps between men and women users’ participation and publication volume. Furthermore, we find that younger users have become increasingly active on the platform over time. We also uncover unique gender-specific behavior patterns that we attempt to characterize and explain. Our findings suggest that the rise of digital health communities has accelerated in recent years, reflecting the public’s growing preference to take a more active role in their medical care.