Specifics Analysis of Medical Communities in Social Network Services

Abstract

Social networks contain a lot of useful medical information in users’ and communities’ posts especially about adverse drug reactions. But before processing of the medical communities, it is important to be aware of their implicit features, which could affect the reliability of the information retrieved. We use the principal component centrality evaluation to reveal features of the distribution of influence of community members. Cosine similarity was used to compare vocabularies and structural indicators of communities of different types. As a result of the research, it was found that the medical communities have significant similarities with the communities of mothers of young children, so they can be used as an extension of the information database on the collection of the drug response. In addition, medical communities may have an atypical structure with several users who have high influence in a particular group, which shows that is necessary to verify the reliability of the information retrieved.