164 Copyright © 2016, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 8 DOI: 10.4018/978-1-4666-8811-7.ch008 ABSTRACT Recent work in machine learning and natural language processing has studied the content of health related information in tweets and demonstrated the potential for extracting useful public health informa- tion from their aggregation. Social intelligence derived from health content has become of signifcant importance for various applications, including post-marketing drug surveillance, competitive intelli- gence, medicine reviews and to assess health-related opinions and sentiments. Further, the quantity of medical information in the media such as tweets on Twitter, Facebook or medical blogs is growing at an exponential rate. Medical data such as health records, drug data, etc. has become major candidates for Big Data analysis and thus exploring this content has become a necessity for organizations. However, the volume, velocity, variety, and quality of online health information present challenges, necessitat- ing enhanced facilitation mechanisms for medical social computing. The objective of this chapter is to discuss the possibility of mining medical trends using Social Networks. INTRODUCTION Social Network is a social structure comprising of individuals (or organizations) interconnected by one or more specific types of interdependencies such as kinship, exchange of financial data (e.g. SWIFT code), communication exchange, and other information or knowledge processing entities. It is based on an assumption of the importance of relationships among interacting units (Wasserman, S. and K. Faust, 1994). Social Network Analysis is the application of graph theory to comprehend, classify and quantify relationships in a social network. Social Network Analysis (SNA) relates to mapping, understanding, and analysing interactions across a set of people. Social network mining approaches tend to be founded on graph mining or network analysis techniques. Due to the evident recent big data surge due to increased Mining of Medical Trends Using Social Networks Shruti Kohli Birla Institute of Technology, India Sonia Saini Birla Institute of Technology, India