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

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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.
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.
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
PublisherIEEE
Date2017
Pages2772-2777
ISBN (Print)9781538627167
ISBN (Electronic)9781538627150, 9781538627143
DOIs
StatePublished - 2017
Event5th IEEE International Conference on Big Data. 2017 - Boston, United States
Duration: 11 Dec 201714 Dec 2017
Conference number: 5
http://cci.drexel.edu/bigdata/bigdata2017/

Conference

Conference5th IEEE International Conference on Big Data. 2017
Number5
CountryUnited States
CityBoston
Period11/12/201714/12/2017
Internet address

Keywords

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

Cite this

Straton, N., Mukkamala, R. R., & Vatrapu, R. (2017). Big Social Data Analytics for Public Health: Comparative Methods Study and Performance Indicators of Health Care Content on Facebook. In J-Y. Nie, Z. Obradovic, T. Suzumura, R. Ghosh, R. Nambiar, C. Wang, H. Zang, R. Baeza-Yates, X. Hu, J. Kepner, A. Cuzzocrea, J. Tang, ... M. Toyoda (Eds.), Proceedings. 2017 IEEE International Conference on Big Data: IEEE Big Data 2017 (pp. 2772-2777). Los Alamitos, CA: IEEE. DOI: 10.1109/BigData.2017.8258243
Straton, Nadiya ; Mukkamala, Raghava Rao ; Vatrapu, Ravi. / Big Social Data Analytics for Public Health : Comparative Methods Study and Performance Indicators of Health Care Content on Facebook. Proceedings. 2017 IEEE International Conference on Big Data: IEEE Big Data 2017. editor / 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. Los Alamitos, CA : IEEE, 2017. pp. 2772-2777
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title = "Big Social Data Analytics for Public Health: Comparative Methods Study and Performance Indicators of Health Care Content on Facebook",
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.",
keywords = "Gaussian mixture model, K nearest neighbors (KNN), BIC (Bayes Information criterion), AIC (Akaike information criterion), CV (Cross Validation), Gaussian mixture model, K nearest neighbors (KNN), BIC (Bayes Information criterion), AIC (Akaike information criterion), CV (Cross Validation)",
author = "Nadiya Straton and Mukkamala, {Raghava Rao} and Ravi Vatrapu",
year = "2017",
doi = "10.1109/BigData.2017.8258243",
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editor = "Jian-Yun Nie and Zoran Obradovic and Toyotaro Suzumura and Rumi Ghosh and Raghunath Nambiar and Chonggang Wang and Hui Zang and Ricardo Baeza-Yates and Xiaohua Hu and Jeremy Kepner and Alfredo Cuzzocrea and Jian Tang and Masashi Toyoda",
booktitle = "Proceedings. 2017 IEEE International Conference on Big Data",
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}

Straton, N, Mukkamala, RR & Vatrapu, R 2017, Big Social Data Analytics for Public Health: Comparative Methods Study and Performance Indicators of Health Care Content on Facebook. in J-Y Nie, Z Obradovic, T Suzumura, R Ghosh, R Nambiar, C Wang, H Zang, R Baeza-Yates, X Hu, J Kepner, A Cuzzocrea, J Tang & M Toyoda (eds), Proceedings. 2017 IEEE International Conference on Big Data: IEEE Big Data 2017. IEEE, Los Alamitos, CA, pp. 2772-2777, Boston, United States, 11/12/2017. DOI: 10.1109/BigData.2017.8258243

Big Social Data Analytics for Public Health : Comparative Methods Study and Performance Indicators of Health Care Content on Facebook. / Straton, Nadiya; Mukkamala, Raghava Rao; Vatrapu, Ravi.

Proceedings. 2017 IEEE International Conference on Big Data: IEEE Big Data 2017. ed. / 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. Los Alamitos, CA : IEEE, 2017. p. 2772-2777.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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T1 - Big Social Data Analytics for Public Health

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AB - 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.

KW - Gaussian mixture model

KW - K nearest neighbors (KNN)

KW - BIC (Bayes Information criterion)

KW - AIC (Akaike information criterion)

KW - CV (Cross Validation)

KW - Gaussian mixture model

KW - K nearest neighbors (KNN)

KW - BIC (Bayes Information criterion)

KW - AIC (Akaike information criterion)

KW - CV (Cross Validation)

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M3 - Article in proceedings

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PB - IEEE

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Straton N, Mukkamala RR, Vatrapu R. Big Social Data Analytics for Public Health: Comparative Methods Study and Performance Indicators of Health Care Content on Facebook. In Nie J-Y, Obradovic Z, Suzumura T, Ghosh R, Nambiar R, Wang C, Zang H, Baeza-Yates R, Hu X, Kepner J, Cuzzocrea A, Tang J, Toyoda M, editors, Proceedings. 2017 IEEE International Conference on Big Data: IEEE Big Data 2017. Los Alamitos, CA: IEEE. 2017. p. 2772-2777. Available from, DOI: 10.1109/BigData.2017.8258243