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.

Publication information

Original languageEnglish
Title of host publicationProceedings of the 6th IEEE International Congress on Big Data. BigData Congress 2017
EditorsGeorge Karypis, Jia Zhang
Number of pages10
Place of PublicationLos Alamitos, CA
Publication date2017
ISBN (print)9781538619964
ISBN (electronic)9781538619957, 9781538619971
StatePublished - 2017
Event - Honolulu, United States


Conference6th IEEE International Congress on Big Data
LocationHilton Hawaiian Village Waikiki Beach Resort
LandUnited States

Bibliographical note

CBS Library does not have access to the material


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

ID: 47271307