Social Media Popularity Prediction: A Multiple Feature Fusion Approach with Deep Neural Networks

Abstract

Social media popularity prediction (SMPD) aims to predict the popularity of the post shared on online social media platforms. This task is crucial for content providers and consumers in a wide range of real-world applications, including multimedia advertising, recommendation system and trend analysis. In this paper, we propose to fuse features from multiple sources by deep neural networks (DNNs) for popularity prediction. Specifically, high-level image and text features are extracted by the advanced pretrained DNN, and numerical features are captured from the metadata of the posts. All of the features are concatenated and fed into a regressor with multiple dense layers. Experiments have demonstrated the effectiveness of the proposed model on the ACM Multimedia Challenge SMPD2019 dataset. We also verify the importance of each feature via univariate test and ablation study, and provide the insights of feature combination for social media popularity prediction.

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