metadata

license: other
license_name: smp-license
license_link: LICENSE.md
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  SMP CHALLENGE COMMUNITY LICENSE AGREEMENT

  Last Updated: March 17, 2025

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language:
  - en
pretty_name: Social Media Prediction - Video
size_categories:
  - 1K<n<10K
configs:
  - config_name: posts
    data_files:
      - split: train
        path: train/SMP-Video_anonymized_posts_train.jsonl
      - split: test
        path: test/SMP-Video_anonymized_posts_test.jsonl
  - config_name: users
    data_files:
      - split: train
        path: train/SMP-Video_anonymized_users_train.jsonl
      - split: test
        path: test/SMP-Video_anonymized_users_test.jsonl
  - config_name: videos
    data_files:
      - split: train
        path: train/SMP-Video_anonymized_videos_train.jsonl
      - split: test
        path: test/SMP-Video_anonymized_videos_test.jsonl
  - config_name: labels
    data_files:
      - split: train
        path: train/SMP-Video_anonymized_popularity_train.jsonl
task_categories:
  - time-series-forecasting

Social Media Prediction Challenge - Video

Part of SMP Challenge: https://smp-challenge.com

Dataset Overview

The SMP-Video dataset supports social media popularity prediction, reflecting real-world challenges in social media analysis. Participants are tasked with predicting the future popularity of anonymized social media video posts.

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 scores provided as prediction targets.

Dataset 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 (without labels).
Video Source: Please visit video-source.

How to Use 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']

About SMP Challenge

Social Media Prediction Challenge (SMP Challenge) is an annual international competition dedicated to advancing research in social multimodal forecasting. It aims to identify outstanding research teams and innovative solutions that can contribute to understanding and predicting user behavior, content virality, and engagement trends across social platforms—ultimately supporting better user experiences and smarter business decisions.

How to join

For detailed information about the SMP Challenge, including task, evaluation, task and ongoing updates, please visit:

These pages provide comprehensive insights into the goals of the SMP Challenge, the tasks involved, and the methodologies participants are encouraged to explore.

Citation

If you like or use the data or our research as part of your work, welcome to encourage us by citing them:

@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{SMPdataset2019,
  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{Wu2017TemporalContext,
    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{Wu2016Prediction,
    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}}