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.
@article{arxiv.2405.10497,
title = {SMP Challenge: An Overview and Analysis of Social Media Prediction Challenge},
author = {Bo Wu and Peiye Liu and Wen-Huang Cheng and Bei Liu and Zhaoyang Zeng and Jia Wang and Qiushi Huang and Jiebo Luo},
journal= {arXiv preprint arXiv:2405.10497},
year = {2024}
}
Comments
ACM Multimedia. arXiv admin note: text overlap with arXiv:1910.01795