Big Social Data Analytics for Public Health

Comparative Methods Study and Performance Indicators of Health Care Content on Facebook

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningpeer review

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Resumé

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
Udgivelses stedLos Alamitos, CA
ForlagIEEE
Publikationsdato2017
Sider2772-2777
ISBN (Trykt)9781538627167
ISBN (Elektronisk)9781538627150, 9781538627143
DOI
StatusUdgivet - 2017
Begivenhed2017 IEEE International Conference on Big Data - Boston, USA
Varighed: 11 dec. 201714 dec. 2017
Konferencens nummer: 5
http://cci.drexel.edu/bigdata/bigdata2017/

Konference

Konference2017 IEEE International Conference on Big Data
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)

Citer dette

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. I 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 (red.), Proceedings. 2017 IEEE International Conference on Big Data: IEEE Big Data 2017 (s. 2772-2777). Los Alamitos, CA: IEEE. https://doi.org/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. red. / 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. s. 2772-2777
@inproceedings{23a76e41e902418d964b7bedf89a9481,
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",
language = "English",
isbn = "9781538627167",
pages = "2772--2777",
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",
publisher = "IEEE",
address = "United States",

}

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. i 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 (red), Proceedings. 2017 IEEE International Conference on Big Data: IEEE Big Data 2017. IEEE, Los Alamitos, CA, s. 2772-2777, 2017 IEEE International Conference on Big Data, Boston, USA, 11/12/2017. https://doi.org/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. red. / 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. s. 2772-2777.

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningpeer review

TY - GEN

T1 - Big Social Data Analytics for Public Health

T2 - Comparative Methods Study and Performance Indicators of Health Care Content on Facebook

AU - Straton, Nadiya

AU - Mukkamala, Raghava Rao

AU - Vatrapu, Ravi

PY - 2017

Y1 - 2017

N2 - 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.

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)

U2 - 10.1109/BigData.2017.8258243

DO - 10.1109/BigData.2017.8258243

M3 - Article in proceedings

SN - 9781538627167

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EP - 2777

BT - Proceedings. 2017 IEEE International Conference on Big Data

A2 - Nie, Jian-Yun

A2 - Obradovic, Zoran

A2 - Suzumura, Toyotaro

A2 - Ghosh, Rumi

A2 - Nambiar, Raghunath

A2 - Wang, Chonggang

A2 - Zang, Hui

A2 - Baeza-Yates, Ricardo

A2 - Hu, Xiaohua

A2 - Kepner, Jeremy

A2 - Cuzzocrea, Alfredo

A2 - Tang, Jian

A2 - Toyoda, Masashi

PB - IEEE

CY - Los Alamitos, CA

ER -

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. I 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, red., Proceedings. 2017 IEEE International Conference on Big Data: IEEE Big Data 2017. Los Alamitos, CA: IEEE. 2017. s. 2772-2777 https://doi.org/10.1109/BigData.2017.8258243