Various online services such as Socialbakers, Keyhole, Gnip offer tools to analyze, fetch and collect data from social media. This data is often presented in a form of interactive web based dashboards, displaying various trends: number of posts, mentions, shares, likes over time. Facebook and Twitter have an API to access data on social media profiles of real people. Users profiles usually have data on age, sex, employment and relationships status, specific group membership, etc. We conducted a simple feasibility study using Facebook API in diabetes area using profiles of people posting hashtags as a primary source of data. We then expanded the sample by adding people who liked, shared and reposted messages containing diabetes relaed hashtags #Diabetes, #dedoc, #ourD. We applied exclusion criteria to derive a sample consisting of patients only, hence targeting specific group of people.Our assumption was that people who interact with posts containing specific hashtag have diabetes. We used descriptive statistics to characterize obtained sample (n=17296) by calculating mean age, age distribution histogram, proportion of males and females and other descriptive metrics. We also calculated conditional probabilities of being in multiple disease area Facebook groups such as obesity groups or groups of people with increased risk of cardiovascular disease.  Future area of research will be concentrated on aspects of in-degree centrality in network of diabetic people, hypothesis testing between two different groups, analyses of changes in positive/negative posting trends following drug launch, locating agents and influencers in the network and conducting prospective studies in social media using hashtags. Social Media data can be a valuable addition to a real life post launch data. Evidence on changes in positive/negative postings can be used as an additional piece of information in Phase IV studies or risk-sharing agreements.