Random Forest Exploiting Post-related and User-related Features for Social Media Popularity Prediction

Abstract

Social media headline prediction (SMHP) is a thriving application scenario, which aims to predict the popularity of the post data shared on social media. In this paper, we propose to use multi-aspect features combined with the random forest (RF) model for popularity predictions. Firstly, we extract features by combining both metadata of the posts and users' features. More specifically, we adopt the binary coding strategy for dimensionality reduction and deal with the missing values by using some strategies, i.e., estimating the missing geographic information according to the information of users, and filling the missing features with median. Furthermore, regression models can be used directly to make predictions. In particular, a random forest (RF) model is adopted since it does not require much effort in tuning hyper-parameters and performs effectively. Extensive experiments conducted on the SMHP dataset consisting of 340K image posts shared by 80K users manifest the effectiveness of our method. Our approach achieves the 4nd place in the leader board of the Grand Challenge of SMHP in ACM Multimedia 2018.