A Cloud Based Framework for Identification of Influential Health Experts from Twitter

The ever increasing growth in health related data has necessitated the development of pervasive tools and technologies to manage the huge data volumes. Likewise, the conventional healthcare services are transforming into patient-centric services to offer ubiquitous access to the health related information. However, there is a need to extend the capabilities of the existing health services and tools so that users could become aware about their health, devise wellness plans, and seek experts´ advice at no or low cost using the social media. In this paper, we propose a cloud based framework that uses Twitter data to offer recommendations about the most influential health experts. We employ a variant of the Hyperlink-Induced Topic Search (HITS) approach to identify the candidate health experts based on health related keywords used in the tweets. Subsequently, we propose an influence metric that calculates the influence of the candidate experts based on various parameters. The proposed approach attained high accuracy when compared to other approaches for expert user identification. Moreover, experimental results exhibit that the approach is highly scalable for workloads of varying sizes.