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Data Mining Techniques on Social Media Drug Related Posts – A Comparative Study & Analysis

D. KrithikaRenuka, Dr. B. Rosiline Jeetha


Intelligently extracting knowledge from social media has recently attracted great interest from the Biomedical and Health Informatics community to simultaneously improve healthcare outcomes and reduce costs using consumer-generated opinion. Social media offers opportunities for patients and doctors to share their opinions and experiences freely in online communities, which may contribute information beyond the knowledge of domain professionals. However, for traditional public health surveillance systems, it is hard to detect and monitor health related concerns and changes in public attitudes to health-related issues. To solve this problem, Multiple studies illustrated the use of information in social media to discover biomedical and health-related knowledge.  Several disease-specific information exchanges now exist on Face book and other online social networking sites. These new sources of knowledge, support, and engagement have become important for patients living with disease, yet the quality and content of the information provided in these digital areas are poorly understood. The existing research methodologies are discussed with their merits and demerits, so that the further research works can be concentrated more. The experimental tests conducted were on all the research works in matlab simulation environment and it is compared against each other to find the better approach under various performance measures such as Accuracy, Precision and Recall.


Social Media, Health Related Issues, Sentiment Classifications and SOM.

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L. Toldo, “Text mining fundamentals for business analytics,” presented at the 11th Annual Text and Social Analytics Summit, Boston, MA, USA, 2013.

Culotta A. Detecting influenza outbreaks by analyzing twitter messages. CoRR abs/1007.4748.

Heaivilin N, Gerbert B, Page J, Gibbs J. Public health surveillance of dental pain via twitter. J Dental Res 2011; 90(9):1047–51.

Blei DM, Ng AY, Jordan MI. Latent Dirichlet allocation. J Mach Learn Res 2003; 3:993–1022.

Paul MJ, Dredze M. A model for mining public health topics from twitter. Tech. rep.; 2011.

Paul MJ, Dredze M. You are what you tweet: analyzing Twitter for public health. In: Fifth international AAAI conference on weblogs and and socialmedia; 2011. p. 265–72.

Corley, Courtney D., et al. "Text and structural data mining of influenza mentions in web and social media." International journal of environmental research and public health 7.2 (2010): 596-615.

Greene J, Choudhry N, Kilabuk E, Shrank W. Online social networking by patients with diabetes: a qualitative evaluation of communication with Facebook. J Gen Internal Med 2011; 26(3):287–92. s11606-010-1526-3.

Hideo Hirose, Liangliang Wan, “Prediction of Infectious Disease Spread using Twitter: A Case of Influenza”, Fifth International Symposium on Parallel Architectures, Algorithms and Programming, 2012

Bodnar, Todd, and Marcel Salathé. "Validating models for disease detection using twitter." Proceedings of the 22nd International Conference on World Wide Web. ACM, 2013.

Cameron, Delroy, et al. "PREDOSE: A semantic web platform for drug abuse epidemiology using social media." Journal of biomedical informatics 46.6 (2013): 985-997.

Yang, Christopher C., et al. "Social media mining for drug safety signal detection." Proceedings of the 2012 international workshop on Smart health and wellbeing. ACM, 2012.

Ji, Xiang, Soon Ae Chun, and James Geller. "Monitoring public health concerns using twitter sentiment classifications." Healthcare Informatics (ICHI), 2013 IEEE International Conference on. IEEE, 2013.

Tuarob, Suppawong, Conrad S. Tucker b,a, , Marcel Salathe c , Nilam Ram d. "An ensemble heterogeneous classification methodology for discovering health-related knowledge in social media messages." Journal of biomedical informatics 49 (2014): 255-268.

Isah, Haruna, Paul Trundle, and Daniel Neagu. "Social media analysis for product safety using text mining and sentiment analysis." 2014 14th UK Workshop on Computational Intelligence (UKCI). IEEE, 2014.

Akay, Altug, Andrei Dragomir, and Björn-Erik Erlandsson. "Network-based modeling and intelligent data mining of social media for improving care."IEEE journal of biomedical and health informatics 19.1 (2015): 210-218.


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