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

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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.
TitelProceedings of the 6th IEEE International Congress on Big Data. BigData Congress 2017
RedaktørerGeorge Karypis, Jia Zhang
Antal sider8
UdgivelsesstedLos Alamitos, CA
ISBN (Trykt)9781538619964
ISBN (Elektronisk)9781538619957, 9781538619971
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


Konference6th IEEE International Congress on Big Data. BigData Congress 2017
LokationHilton Hawaiian Village Waikiki Beach Resort


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