Thursday, June 22, 1:00pm, POST 302
Comparative Methods Study on Predicting User Engagement with Health Care Content on Social Media
Department of IT Management
Copenhagen Business School
Facebook “post popularity” analysis is fundamental for differentiating between relevant posts and posts with low user engagement. This research study aims at health and care organizations to improve information dissemination on social media platforms. At the same time, it will help users navigate through vast amounts of information in direction of the relevant health and care content and resort to preventive measures, where possible. Furthermore, the 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), Deep Neural Networks (DNN) and K-nearest neighbors (KNN). Different network topology is used to achieve best accuracy prediction followed by examples and discussion on why DNN might not be optimal technique for the given data set.