Now days social media are becoming day to day part of our life. It can be observed from the number of users increased on social Media site like Facebook, Twitter, YouTube… etc. Social media is a large collection of data for user views and expressions which cannot be ignored by data analyzer, Policymaker, NGO's and organizations based on people view. Social media data gives very generic and valuable information about problem in society which is studied and problem can be fixed. Social media data is vast, unstructured, noisy and dynamic in nature, and thus novel challenges arise. This paper reviews some method adapted by researcher to collect, classify and analyzing social media data for understanding some of the targeted issues in society.

1. Kaplan Andreas M. , Haenlein Michael (2010) Business Horizons 53, "Users of the world, unite! The challenges and opportunities of social media".
2. D. Gaffney, Proc. Extending the Frontier of Society On-Line (WebSci10), 2010. "#iranElection: Quantifying Online Activism".
3. S. Jamison-Powell, C. Linehan, L. Daley, A. Garbett, and S. Lawson, Proc. ACM Ann. Conf. Human Factors in Computing Systems, pp. 1501-1510, 2012. "'I Can't Get No Sleep': Discuss in #Insomnia on Twitter".
4. M. J. Culnan, P. J. McHugh, and J. I. Zubillaga, MIS Quarterly Executive, vol. 9, no. 4, pp. 243-259,210 "How Large US Companies Can Use Twitter and Other Social Media to Gain Business Value".
5. M. E. Hambrick, J. M. Simmons, G. P. Greenhalgh, and T. C. Greenwell, Int'l J. Sport Comm. , vol. 3, no. 4, pp. 454-471, 2010. "Understanding Professional Athletes' Use of Twitter: A Content Analysis of Athlete Tweets".
6. Xin Chen, Student Member, IEEE, Mihaela Vorvoreanu, and Krishna Madhavan. IEEE Transactions on learning technologies, July-September 2014, "Mining Social Media Data Understanding Students' Learning Experiences ".
7. M. Rost, L. Barkhuus, H. Cramer, and B. Brown, Proc. Conf. Computer Supported Cooperative Work, pp. 357-362, 2013,"Representation and Communication: Challenges in Interpreting Large Social Media Datasets".
8. J. L. Devore, Probability & Statistics for Engineering and the Sciences. Duxbury Press, 2012.
9. V. Van Asch, "Macro and Micro Averaged Evaluation Measureslys," 2012 http://www. cnts. ua. ac. be/$vincent/pdf/ microaverage. pdf
10. Christian Ehnis, Deborah Bunker, 23rd Australasian Conference on Information Systems, " Social Media in disaster Response: Queenland Police Service-Public Engagement During 2011Floods.
11. Maria Jose L, IEEE transactions on professional communication, Vol 48, no3, september2005, "Genre analysis in technical communication".
12. Cross, R. , Borgatti, S. , & Parker, A. (2003). Making Invisible Work Visible. . In R. Cross, A. Parker & L. Sasson (Eds. ), Networks in the Knowledge Economy (pp. 261-282). New York: Oxford Press.
13. Borgatti, Everett & Freeman 2002, "UCINet for windows software for social network analysis" user guide.
14. V Batagelj and A Mrvar, university of Ljubljana 1997 ~1999 "Pajek-Program for Large Network Analysis".
15. Munmun Choudhary and Scott Counts, Microsoft research, " Understanding Affect in workspace via Social media".
16. Pennebaker, J. W. , Mehl, M. R. , & Niederhoffer, K. (2003). Annual Review of Psychology, 54, 547-577. Psychological aspects of natural language use: Our words,ourselves.

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