Big Social Data Analytics for Public Health

Predicting Facebook Post Performance Using Artificial Neural Networks and Deep Learning

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

Resumé

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 measures
are used to achieve best accuracy prediction followed by examples and discussion on why DNN might not be optimal technique for the given data set.
OriginalsprogEngelsk
TitelProceedings of the 6th IEEE International Congress on Big Data. BigData Congress 2017
RedaktørerGeorge Karypis, Jia Zhang
Udgivelses stedLos Alamitos, CA
ForlagIEEE
Publikationsdato2017
Sider89-96
ISBN (Trykt)9781538619964
ISBN (Elektronisk)9781538619957, 9781538619971
DOI
StatusUdgivet - 2017
Begivenhed6th IEEE International Congress on Big Data. BigData Congress 2017 - Hilton Hawaiian Village Waikiki Beach Resort, Honolulu, USA
Varighed: 25 jun. 201730 jun. 2017
Konferencens nummer: 6
http://www.bigdatacongress.org/2017/

Konference

Konference6th IEEE International Congress on Big Data. BigData Congress 2017
Nummer6
LokationHilton Hawaiian Village Waikiki Beach Resort
LandUSA
ByHonolulu
Periode25/06/201730/06/2017
Internetadresse

Bibliografisk note

CBS Bibliotek har ikke adgang til materialet

Emneord

  • Post performance
  • Artificial neural network (ANN)
  • Deep neural network (DNN)
  • Negative entropy
  • Purity

Citer dette

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. I G. Karypis, & J. Zhang (red.), Proceedings of the 6th IEEE International Congress on Big Data. BigData Congress 2017 (s. 89-96). Los Alamitos, CA: IEEE. https://doi.org/10.1109/BigDataCongress.2017.21
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. red. / George Karypis ; Jia Zhang. Los Alamitos, CA : IEEE, 2017. s. 89-96
@inproceedings{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",
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year = "2017",
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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. i G Karypis & J Zhang (red), Proceedings of the 6th IEEE International Congress on Big Data. BigData Congress 2017. IEEE, Los Alamitos, CA, s. 89-96, 6th IEEE International Congress on Big Data. BigData Congress 2017, Honolulu, USA, 25/06/2017. https://doi.org/10.1109/BigDataCongress.2017.21

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. red. / George Karypis; Jia Zhang. Los Alamitos, CA : IEEE, 2017. s. 89-96.

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

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.

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KW - Negative entropy

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