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

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Abstrakt

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.
OriginalsprogEngelsk
TitelProceedings. 2017 IEEE International Conference on Big Data : IEEE Big Data 2017
RedaktørerJian-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
UdgivelsesstedLos Alamitos, CA
ForlagIEEE
Publikationsdato2017
Sider2772-2777
ISBN (Trykt)9781538627167
ISBN (Elektronisk)9781538627150, 9781538627143
DOI
StatusUdgivet - 2017
BegivenhedFifth IEEE International Conference on Big Data. IEEE BigData 2017 - Boston, USA
Varighed: 11 dec. 201714 dec. 2017
Konferencens nummer: 5
http://cci.drexel.edu/bigdata/bigdata2017/

Konference

KonferenceFifth IEEE International Conference on Big Data. IEEE BigData 2017
Nummer5
LandUSA
ByBoston
Periode11/12/201714/12/2017
Internetadresse

Emneord

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

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