INTRODUCTION
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This Agreement is intended to allow research, non-commercial, and limited commercial uses of the SMP Challenge Materials free of charge. In order to ensure that certain limited commercial uses of the Materials continue to be allowed, this Agreement preserves free access to the Materials for people or organizations generating annual revenue of less than US $50,000 (or local currency equivalent).
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COMMERCIAL USE LICENSE
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"Agreement" means this SMP Challenge Community License Agreement.

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Social Media Prediction Challenge - Video

This dataset is part of the Social Media Prediction Challenge (SMP Challenge), which invites participants to forecast the future popularity of social media posts. In this edition, the focus is on video content, and the dataset provides anonymized data on posts, users, videos, and popularity labels.

Overview

The challenge is organized to promote research in social media forecasting and to benchmark models on predicting time-series trends in online engagement. The dataset comprises the following components:

Posts: Anonymized information regarding the social media posts.
Users: Anonymized user profiles who interact with the posts.
Videos: Anonymized metadata and content-related features of the videos.
Labels: Popularity metrics provided as the target variable for training.

Dataset Structure and Splits

The dataset is divided into three splits to facilitate model training and evaluation:

Train: Contains the training data with full annotations.
Test: Used for evaluation during the challenge.

How to Load the Dataset

You can load the dataset using the Hugging Face datasets library as follows:

from datasets import load_dataset

# Load Train dataset
ds_train_posts = load_dataset("smpchallenge/SMP-Video", 'posts')['train']
ds_train_users = load_dataset("smpchallenge/SMP-Video", 'users')['train']
ds_train_videos = load_dataset("smpchallenge/SMP-Video", 'videos')['train']
ds_train_labels = load_dataset("smpchallenge/SMP-Video", 'labels')['train']

# Load test dataset
ds_test_posts = load_dataset("smpchallenge/SMP-Video", 'posts')['test']
ds_test_users = load_dataset("smpchallenge/SMP-Video", 'users')['test']
ds_test_videos = load_dataset("smpchallenge/SMP-Video", 'videos')['test']

SPM Challenge

For more information about the evaluation and challenge, including rules, dataset details, and updates, please visit:

These pages provide additional insights into the objectives of the SMP Challenge and the methodologies employed.

Citation

If you use this dataset in your research, please cite it as follows:

@inproceedings{SMPAnalysis2023,
  title={SMP Challenge: An Overview and Analysis of Social Media Prediction Challenge},
  author={Wu, Bo and Liu, Peiye and Cheng, Wen-Huang and Liu, Bei and Zeng, Zhaoyang and Wang, Jia and Huang, Qiushi and Luo, Jiebo},
  booktitle={Proceedings of the 31st ACM International Conference on Multimedia},
  year={2023}}
@inproceedings{SMP2019,
  author = {Wu, Bo and Cheng, Wen-Huang and Liu, Peiye and Liu, Bei and Zeng, Zhaoyang and Luo, Jiebo},
  title = {SMP Challenge: An Overview of Social Media Prediction Challenge 2019},
  booktitle={Proceedings of the 27th ACM International Conference on Multimedia},
  year = {{2019}}
@inproceedings{Wu2017DTCN,
    title={Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks},
    author={Wu, Bo and Cheng, Wen-Huang and Zhang, Yongdong and Qiushi, Huang and Jintao, Li and Mei, Tao},
    booktitle={International Joint Conference on Artificial Intelligence (IJCAI)},
    year={2017}}
@inproceedings{Wu2016TemporalPrediction,
    author = {Wu, Bo and Mei, Tao and Cheng, Wen-Huang and Zhang, Yongdong},
    title = {Unfolding Temporal Dynamics: Predicting Social Media Popularity Using Multi-scale Temporal Decomposition},
    booktitle = {Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI)}
    year = {2016}}

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