Journal
2017 IEEE 6TH INTERNATIONAL CONGRESS ON BIG DATA (BIGDATA CONGRESS 2017)
Volume -, Issue -, Pages 89-96Publisher
IEEE
DOI: 10.1109/BigDataCongress.2017.21
Keywords
Post Performance; Artificial Neural Network (ANN); Deep Neural Network (DNN); Negative Entropy; Purity
Categories
Funding
- project Big Social Data Analytics for Public Health: Copenhagen Health Innovation: ReVUS - Region Hovedstaden and Copenhagen Commune
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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.
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