International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 07 | July -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 ISO 9001:2008 Certified Journal | Page 1641 Mining Social Media Data for Understanding Drugs Usage Shadma Qureshi 1 ,Sonal Rai 2 ,Shiv Kumar 3 1 M.Tech Scholar, Department of Computer Science & Engineering, Lakshmi Narain College of Technology & Excellence Bhopal (M.P), India, 2 Assistant Professor, Department of CSE, Lakshmi Narain College of Technology & Excellence, Bhopal (M.P), India 3 Professor & Head, Department of CSE, Lakshmi Narain College of Technology & Excellence, Bhopal (M.P), india -------------------------------------------------------------------------------***------------------------------------------------------------------------------ Abstract: This review/survey paper based on the research carried out in the area of data mining depends for managing bulk amount of data with mining in social media on using composite applications for performing more sophisticated analysis using cloud platform. Enhancement of social media may address this need. The objective of this paper is to introduce such type of tool which used in social network to characterised drug abuse. This paper outlined a structured approach to analyse social media in order to capture emerging trends in drug abuse by applying powerful methods like cloud computing and Map Reduce model. This paper describes how to fetch important data for analysis from social network as Twitter, Facebook, and Instagram. Then big data techniques to extract useful content for analysis are discussed. Keywords: social media; data mining; Big data; illicit drug use; Hadoop; HDFS; clavier. 1. INTRODUCTION Social network (media) is one to extracting the information from the internet. Nowadays it is used for extracting the data of patient’s to know the understanding of patient symptoms. Social media, classify from individual messaging to live for as, is providing immeasurable opportunities for patient to converse their experiences with drug and devices. Social media allows message contribution, gathering information and distribution in the health care space. Health care is one which contains the information of patients with their permission. It provides an effective social networking environment. The proper way of mining information and drift from the knowledge is cloud. Using network based analysis method it model the social media such as Facebook, Twitter, WebMD [1].Nowadays the scientific experiment often requires bulk amount of computation during simulation and data processing. Performance of super computer is increasing rapidly.It allows solving scientific problem by automatic computational through collection or array list which is emerged by set of sensors. Nowadays electronic mechanism is growing in recent scenario. [5] 1.1 Data Mining Techniques The knowledge extracted allows predicting the behaviour and future behaviour. This allows the business owners to take positive knowledge drive decisions. Data mining is enforced in different domain like FMCG, economy, medical, education system etc. Knowledge is derived from the previously fact by applying pattern recognition, statistical, mathematical techniques those results in expertise form of facts, trends, association, patterns, anomalies and exceptions. There are some areas where data mining is applied. Data Pre-processing: Data pre-processing make ready the natural data for mining process. Data Mining: Mining is the way of separating some important patterns from a large amount of data. Pattern Evaluation: This process evaluates the pattern that is generated by the data mining. The patterns are evaluated according to engagingness measure given by user or system. Knowledge presentation: Knowledge presentation uses visualization techniques that visualize the interesting patterns and help the user to notice and interpret the resultant patterns. [ ] Figure 1: utilization of Data mining Industries [ ] Applicatio ns Statistics Database System Pattern recognition Machine Learning Data Mining Statistics Data Warehous e Algorithms