Predicting User Engagement on Social Media: A Comparative Study of Machine Learning-based Modeling Approaches

Abstract

Abstract

Even while mobile social apps are becoming more and more important in people's daily lives, little is known about the elements that motivate users to interact with these apps. The goal of this work is to analyze and define the transition patterns of individual users' in-app actions as a temporally changing action graph. According to our investigation, action graphs are a useful tool for characterizing user behaviour patterns and offering ideas for further interaction. In order to record in-app usage patterns, extract multiple high-order graph elements. To further improve prediction power by developing an end-to-end, multi-channel neural model that encodes activity sequences, temporal action graphs, and other macroscopic features. Machine learning (ML) has emerged as a powerful technique in a number of predictive analytics domains, including social media data analysis. Social media platforms have grown into massive user-generated content databases that offer valuable insights into the interests, behaviors, and trends of their users. To apply machine learning algorithms to anticipate user behaviour based on social media data and detect important trends. The study makes use of a substantial dataset that comprises user profiles, blog posts, comments, and engagement metrics acquired from well-known social networking sites. Predictive models are developed using a variety of machine learning algorithms, including ensemble approaches, neural networks, decision trees, and support vector machines. Future work in this area must concentrate on resolving privacy and data quality concerns related to social media data in order to improve machine learning's prediction powers.

Keywords: Modeling Approaches, Machine Learning, multi-channel neural model, algorithms, sparser transitions