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A Survey and Analysis of Various Health-Related Knowledge Mining Techniques in Social Media
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 158 - Number 1 |
| Year of Publication: 2017 |
| Authors: D. Krithika Renuka, B. Rosiline Jeetha |
10.5120/ijca2017912718
|
D. Krithika Renuka, B. Rosiline Jeetha . A Survey and Analysis of Various Health-Related Knowledge Mining Techniques in Social Media. International Journal of Computer Applications. 158, 1 ( Jan 2017), 5-10. DOI=10.5120/ijca2017912718
Abstract
Smart extraction of knowledge from social media has received the recent interest of the Biomedical and Health Informatics community for the simultaneous improvement of healthcare outcomes and lessen the expenses making use of consumer-generated reviews. Social media provides chances for patients and doctors to share their views and experiences without any obtrusion through online communities that might generate information, which is much beyond what is known by the domain experts. Nonetheless, for conventional public health surveillance systems, it is difficult to detect and then monitor the concerns related to health and the changes seen in attitudes of the public towards health-related problems. To solve this problem, several studies have shown the usage of information in social media for the discovery of biomedical and health-related information. Several disease-specific knowledge exchanges are now available on Face book and other portals of online social networking. These kind of new sources of information, support, and engagement have gone to become significant for patients who are suffering with the disease, and still the quality and the content of the knowledge contributed in these digital areas are not properly comprehended. 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 mat lab simulation environment and it is compared against each other to find the better approach under various performance measures such as Accuracy, Precision and Recall.
References
- Toldo L., 2013. “Text mining fundamentals for business analytics”, presented at the 11th Annual Text and Social Analytics Summit, Boston, MA, USA.
- Culotta, A., “Detecting influenza outbreaks by analyzing twitter messages”, CoRR abs/1007.4748.
- Heaivilin, N., Gerbert, B., Page, J and Gibbs, J., 2011. “Public health surveillance of dental pain via twitter”, J Dental Res., 90(9):1047–51.
- Blei, D.M., Ng, A.Y and Jordan, M.I., 2003. “Latent Dirichlet allocation”, J Mach Learn Res, 3:993–1022.
- Paul, M.J and Dredze M., 2011. “A model for mining public health topics from twitter”.
- Paul, M.J and Dredze, M, 2011. “You are what you tweet: analyzing Twitter for public health”, In: Fifth international AAAI conference on weblogs and and social media, 265–72.
- Corley and Courtney D, 2010. “Text and structural data mining of influenza mentions in web and social media”, International journal of environmental research and public health, 596-615.
- Greene, J., Choudhry, N., Kilabuk, E and Shrank, W, 2011. “Online social networking by patients with diabetes: a qualitative evaluation of communication with Facebook”, 26(3):287–92.
- Hideo Hirose and 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”, In International Conference on Proceedings of the 22nd World Wide Web, 2013.
- Cameron and Delroy, 2013. “PREDOSE: A semantic web platform for drug abuse epidemiology using social media”, Journal of biomedical informatics, 985-997.
- Yang and Christopher C., 2012. “Social media mining for drug safety signal detection”, Proceedings of the 2012 international workshop on Smart health and wellbeing, ACM.
- Ji, Xiang, Soon Ae Chun and James Geller, 2013. “Monitoring public health concerns using Twitter sentiment classifications”, In International Conference Healthcare Informatics.
- Tuarob, S., Tucker, C.S., Salathe, M and Ram, N., 2014. “An ensemble heterogeneous classification methodology for discovering health-related knowledge in social media messages”, Journal of biomedical informatics, 49:255-268.
- Isah, H., Trundle, P and Neagu, D., 2014. “Social media analysis for product safety using text mining and sentiment analysis”, In 2014 14th UK Workshop on Computational Intelligence, 1-7.
- Akay, A., Dragomir, A and Erlandsson, B.E. 2015 , “Network-based modeling and intelligent data mining of social media for improving care”, IEEE journal of biomedical and health informatics, 19(1):210-21
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