SMP Challenge: An Overview and Analysis of Social Media Prediction Challenge
This work addresses the need for standardized benchmarks in social media popularity prediction, which is incremental as it builds on existing challenges and datasets.
The paper tackles the problem of predicting social media post popularity by summarizing the SMP Challenge and releasing a large-scale benchmark dataset with around 500,000 posts from 70,000 users, facilitating model evaluation and analysis of recent research trends.
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.