Big Social Data Analytics for Public Health: Comparative Methods Study and Performance Indicators of Health Care Content on Facebook

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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 languageEnglish
Title of host publicationProceedings. 2017 IEEE International Conference on Big Data : IEEE Big Data 2017
EditorsJian-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 PublicationLos Alamitos, CA
Publication date2017
ISBN (Print)9781538627167
ISBN (Electronic)9781538627150, 9781538627143
Publication statusPublished - 2017
EventFifth IEEE International Conference on Big Data. IEEE BigData 2017 - Boston, United States
Duration: 11 Dec 201714 Dec 2017
Conference number: 5


ConferenceFifth IEEE International Conference on Big Data. IEEE BigData 2017
Country/TerritoryUnited States
Internet address


  • Gaussian mixture model
  • K nearest neighbors (KNN)
  • BIC (Bayes Information criterion)
  • AIC (Akaike information criterion)
  • CV (Cross Validation)

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