Big Social Data Analytics for Public Health: Predicting Facebook Post Performance Using Artificial Neural Networks and Deep Learning

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

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 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.
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.
LanguageEnglish
Title of host publicationProceedings of the 6th IEEE International Congress on Big Data. BigData Congress 2017
EditorsGeorge Karypis, Jia Zhang
Place of PublicationLos Alamitos, CA
PublisherIEEE
Date2017
Pages89-96
ISBN (Print)9781538619964
ISBN (Electronic)9781538619957, 9781538619971
DOIs
StatePublished - 2017
Event6th IEEE International Congress on Big Data. BigData Congress 2017 - Hilton Hawaiian Village Waikiki Beach Resort, Honolulu, United States
Duration: 25 Jun 201730 Jun 2017
Conference number: 6
http://www.bigdatacongress.org/2017/

Conference

Conference6th IEEE International Congress on Big Data. BigData Congress 2017
Number6
LocationHilton Hawaiian Village Waikiki Beach Resort
CountryUnited States
CityHonolulu
Period25/06/201730/06/2017
Internet address

Bibliographical note

CBS Library does not have access to the material

Keywords

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

Cite this

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
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. editor / George Karypis ; Jia Zhang. Los Alamitos, CA : IEEE, 2017. pp. 89-96
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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.",
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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. 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, Honolulu, United States, 25/06/2017. DOI: 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. ed. / George Karypis; Jia Zhang. Los Alamitos, CA : IEEE, 2017. p. 89-96.

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

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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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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