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.

社交媒体人气预测 (SMPP) 是一个关键任务，涉及利用社交媒体平台上的大量多模态数据自动预测在线帖子的未来人气值。本研究总结了具有挑战性的任务、数据和研究进展，提供了整体分析和最新信息。

SMP 挑战：社交媒体预测挑战的概述与分析

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

"SMP Challenge" aims to discover novel prediction tasks for numerous data on
social multimedia and seek excellent research teams. Making predictions via
social multimedia data (e.g. photos, videos or news) is not only helps us to
make better strategic decisions for the future, but also explores advanced
predictive learning and analytic methods on various problems and scenarios,
such as multimedia recommendation, advertising system, fashion analysis etc.
In the SMP Challenge at ACM Multimedia 2019, we introduce a novel prediction
task Temporal Popularity Prediction, which focuses on predicting future
interaction or attractiveness (in terms of clicks, views or likes etc.) of new
online posts in social media feeds before uploading. We also collected and
released a large-scale SMPD benchmark with over 480K posts from 69K users. In
this paper, we define the challenge problem, give an overview of the dataset,
present statistics of rich information for data and annotation and design the
accuracy and correlation evaluation metrics for temporal popularity prediction
to the challenge.

本文介绍了 “SMP Challenge” 中的新预测任务 ——“时序流行度预测”，该任务旨在利用社交多媒体数据进行未来互动性或吸引力的预测，并发布了一个包含超过 480K 发布内容和 69K 用户的大型 SMPD 基准数据集。