An Effective Text-based Characterization Combined with Numerical Features for Social Media Headline Prediction

Abstract

In this paper, a text-based characterization combined with numerical features for Social Media Headline Prediction (SMHP) is proposed. Description of images, users' emotions and opinions are all described in text, our text-based characterization learns these important features by training a Doc2vec model. Numerical features of social cues contain general characteristics of social media headline, we build an effective method to extract numerical features. Experiments conducted on real-world SMHP dataset manifest the effectiveness of the proposed approach, which achieves the following performance: Spearmanr's Rho: 0.4559, MAE:1.9797.