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Big Social Data Analytics for Public Health : Predicting Facebook Post Performance Using Artificial Neural Networks and Deep Learning. / Straton, Nadiya; Mukkamala, Raghava Rao; Vatrapu, Ravi.

Proceedings of the 6th IEEE International Congress on Big Data. BigData Congress 2017. ed. / George Karypis; Jia Zhang. Los Alamitos, CA : IEEE, 2017. p. 89-96.

Publication: Research - peer-reviewArticle in proceedings

Harvard

Straton, N, Mukkamala, RR & Vatrapu, R 2017, Big Social Data Analytics for Public Health: Predicting Facebook Post Performance Using Artificial Neural Networks and Deep Learning. in G Karypis & J Zhang (eds), Proceedings of the 6th IEEE International Congress on Big Data. BigData Congress 2017. IEEE, Los Alamitos, CA, pp. 89-96. DOI: 10.1109/BigDataCongress.2017.21

APA

Straton, N., Mukkamala, R. R., & Vatrapu, R. (2017). Big Social Data Analytics for Public Health: Predicting Facebook Post Performance Using Artificial Neural Networks and Deep Learning. In G. Karypis, & J. Zhang (Eds.), Proceedings of the 6th IEEE International Congress on Big Data. BigData Congress 2017 (pp. 89-96). Los Alamitos, CA: IEEE. DOI: 10.1109/BigDataCongress.2017.21

CBE

Straton N, Mukkamala RR, Vatrapu R. 2017. Big Social Data Analytics for Public Health: Predicting Facebook Post Performance Using Artificial Neural Networks and Deep Learning. Karypis G, Zhang J, editors. In Proceedings of the 6th IEEE International Congress on Big Data. BigData Congress 2017. Los Alamitos, CA: IEEE. pp. 89-96. Available from: 10.1109/BigDataCongress.2017.21

MLA

Straton, Nadiya, Raghava Rao Mukkamala, and Ravi Vatrapu "Big Social Data Analytics for Public Health: Predicting Facebook Post Performance Using Artificial Neural Networks and Deep Learning". and Karypis, George Zhang, Jia (ed.). Proceedings of the 6th IEEE International Congress on Big Data. BigData Congress 2017. Los Alamitos, CA: IEEE. 2017. 89-96. Available: 10.1109/BigDataCongress.2017.21

Vancouver

Straton N, Mukkamala RR, Vatrapu R. Big Social Data Analytics for Public Health: Predicting Facebook Post Performance Using Artificial Neural Networks and Deep Learning. In Karypis G, Zhang J, editors, Proceedings of the 6th IEEE International Congress on Big Data. BigData Congress 2017. Los Alamitos, CA: IEEE. 2017. p. 89-96. Available from, DOI: 10.1109/BigDataCongress.2017.21

Author

Straton, Nadiya; Mukkamala, Raghava Rao; Vatrapu, Ravi / Big Social Data Analytics for Public Health : Predicting Facebook Post Performance Using Artificial Neural Networks and Deep Learning.

Proceedings of the 6th IEEE International Congress on Big Data. BigData Congress 2017. ed. / George Karypis; Jia Zhang. Los Alamitos, CA : IEEE, 2017. p. 89-96.

Publication: Research - peer-reviewArticle in proceedings

Bibtex

@inbook{afed6ec8a32a476e9a2a60ef4cadbb21,
title = "Big Social Data Analytics for Public Health: Predicting Facebook Post Performance Using Artificial Neural Networks and Deep Learning",
abstract = "Facebook ”post popularity” analysis is fundamental for differentiating between relevant posts and posts with low user engagement and consequently their characteristics. This research study aims at health and care organizations to improve information dissemination on social media platforms by reducing clutter and noise. At the same time, it will help users navigate through vast amount of information in direction of the relevant health and care content. Furthermore, study explores prediction of popularity of healthcare posts on the largest social media platform Facebook. Methodology is presented in this paper to predict user engagement based on eleven characteristics of the post: Post Type, Hour Span, Facebook Wall Category, Level, Country, isHoliday, Season, Created Year, Month, Day of the Week, Time of the Day. Finally, post performance prediction is conducted using Artificial Neural Networks (ANN) and Deep Neural Networks (DNN). Different network topology measuresare used to achieve best accuracy prediction followed by examples and discussion on why DNN might not be optimal technique for the given data set.",
keywords = "Post performance, Artificial neural network (ANN), Deep neural network (DNN), Negative entropy, Purity, Post performance, Artificial neural network (ANN), Deep neural network (DNN), Negative entropy, Purity",
author = "Nadiya Straton and Mukkamala, {Raghava Rao} and Ravi Vatrapu",
note = "CBS Library does not have access to the material",
year = "2017",
doi = "10.1109/BigDataCongress.2017.21",
isbn = "9781538619964",
pages = "89--96",
editor = "George Karypis and Jia Zhang",
booktitle = "Proceedings of the 6th IEEE International Congress on Big Data. BigData Congress 2017",
publisher = "IEEE",
address = "United States",

}

RIS

TY - GEN

T1 - Big Social Data Analytics for Public Health

T2 - Predicting Facebook Post Performance Using Artificial Neural Networks and Deep Learning

AU - Straton,Nadiya

AU - Mukkamala,Raghava Rao

AU - Vatrapu,Ravi

N1 - CBS Library does not have access to the material

PY - 2017

Y1 - 2017

N2 - Facebook ”post popularity” analysis is fundamental for differentiating between relevant posts and posts with low user engagement and consequently their characteristics. This research study aims at health and care organizations to improve information dissemination on social media platforms by reducing clutter and noise. At the same time, it will help users navigate through vast amount of information in direction of the relevant health and care content. Furthermore, study explores prediction of popularity of healthcare posts on the largest social media platform Facebook. Methodology is presented in this paper to predict user engagement based on eleven characteristics of the post: Post Type, Hour Span, Facebook Wall Category, Level, Country, isHoliday, Season, Created Year, Month, Day of the Week, Time of the Day. Finally, post performance prediction is conducted using Artificial Neural Networks (ANN) and Deep Neural Networks (DNN). Different network topology measuresare used to achieve best accuracy prediction followed by examples and discussion on why DNN might not be optimal technique for the given data set.

AB - Facebook ”post popularity” analysis is fundamental for differentiating between relevant posts and posts with low user engagement and consequently their characteristics. This research study aims at health and care organizations to improve information dissemination on social media platforms by reducing clutter and noise. At the same time, it will help users navigate through vast amount of information in direction of the relevant health and care content. Furthermore, study explores prediction of popularity of healthcare posts on the largest social media platform Facebook. Methodology is presented in this paper to predict user engagement based on eleven characteristics of the post: Post Type, Hour Span, Facebook Wall Category, Level, Country, isHoliday, Season, Created Year, Month, Day of the Week, Time of the Day. Finally, post performance prediction is conducted using Artificial Neural Networks (ANN) and Deep Neural Networks (DNN). Different network topology measuresare used to achieve best accuracy prediction followed by examples and discussion on why DNN might not be optimal technique for the given data set.

KW - Post performance

KW - Artificial neural network (ANN)

KW - Deep neural network (DNN)

KW - Negative entropy

KW - Purity

KW - Post performance

KW - Artificial neural network (ANN)

KW - Deep neural network (DNN)

KW - Negative entropy

KW - Purity

U2 - 10.1109/BigDataCongress.2017.21

DO - 10.1109/BigDataCongress.2017.21

M3 - Article in proceedings

SN - 9781538619964

SP - 89

EP - 96

BT - Proceedings of the 6th IEEE International Congress on Big Data. BigData Congress 2017

PB - IEEE

ER -