SMP Challenge: An Overview and Analysis of Social Media Prediction Challenge

TL;DR

This paper reviews the SMP Challenge, a key benchmark in social media popularity prediction, highlighting its data, progress, and the importance of multimodal analysis for future research.

Contribution

It provides a comprehensive overview of the SMP Challenge, including dataset release, analysis of recent solutions, and insights into trends in social media popularity prediction.

Findings

Introduction of a large-scale SMPD benchmark with 500,000 posts

Analysis of recent research solutions and trends

Evaluation of predictive model performance over the years

Abstract

Social Media Popularity Prediction (SMPP) is a crucial task that involves automatically predicting future popularity values of online posts, leveraging vast amounts of multimodal data available on social media platforms. Studying and investigating social media popularity becomes central to various online applications and requires novel methods of comprehensive analysis, multimodal comprehension, and accurate prediction. SMP Challenge is an annual research activity that has spurred academic exploration in this area. This paper summarizes the challenging task, data, and research progress. As a critical resource for evaluating and benchmarking predictive models, we have released a large-scale SMPD benchmark encompassing approximately half a million posts authored by around 70K users. The research progress analysis provides an overall analysis of the solutions and trends in recent years. The SMP Challenge website (www.smp-challenge.com) provides the latest information and news.