Abstract
This paper presents a novel approach that evaluates the right model for post engagement and predictions on Facebook. Moreover, paper provides insight into relevant indicators that lead to higher engagement with health care posts on Facebook. Both supervised and unsupervised learning techniques are used to achieve this goal. This research aims to contribute to strategy of health-care organizations to engage regular users and build preventive mechanisms in the long run through informative health-care content posted on Facebook.
Original language | English |
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Title of host publication | Proceedings. 2017 IEEE International Conference on Big Data : IEEE Big Data 2017 |
Editors | Jian-Yun Nie, Zoran Obradovic, Toyotaro Suzumura, Rumi Ghosh, Raghunath Nambiar, Chonggang Wang, Hui Zang, Ricardo Baeza-Yates, Xiaohua Hu, Jeremy Kepner, Alfredo Cuzzocrea, Jian Tang, Masashi Toyoda |
Place of Publication | Los Alamitos, CA |
Publisher | IEEE |
Publication date | 2017 |
Pages | 2772-2777 |
ISBN (Print) | 9781538627167 |
ISBN (Electronic) | 9781538627150, 9781538627143 |
DOIs | |
Publication status | Published - 2017 |
Event | Fifth IEEE International Conference on Big Data. IEEE BigData 2017 - Boston, United States Duration: 11 Dec 2017 → 14 Dec 2017 Conference number: 5 http://cci.drexel.edu/bigdata/bigdata2017/ |
Conference
Conference | Fifth IEEE International Conference on Big Data. IEEE BigData 2017 |
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Number | 5 |
Country/Territory | United States |
City | Boston |
Period | 11/12/2017 → 14/12/2017 |
Internet address |
Keywords
- Gaussian mixture model
- K nearest neighbors (KNN)
- BIC (Bayes Information criterion)
- AIC (Akaike information criterion)
- CV (Cross Validation)