Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
97 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

SMP Challenge: An Overview and Analysis of Social Media Prediction Challenge (2405.10497v1)

Published 17 May 2024 in cs.MM, cs.AI, cs.CV, and cs.SI

Abstract: 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.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (36)
  1. Enriching word vectors with subword information. Transactions of the association for computational linguistics (2017).
  2. Latent Factors of Visual Popularity Prediction. In Proceedings of ACM International Conference on Multimedia Retrieval.
  3. Social media popularity prediction based on visual-textual features with xgboost. In Proceedings of ACM international conference on Multimedia (ACM MM). 2692–2696.
  4. Tianqi Chen and Carlos Guestrin. 2016. XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
  5. Curriculum Learning for Wide Multimedia-Based Transformer with Graph Target Detection. In Proceedings of ACM international conference on Multimedia (ACM MM). 4575–4579.
  6. Title-and-Tag Contrastive Vision-and-Language Transformer for Social Media Popularity Prediction. In Proceedings of the 30th ACM International Conference on Multimedia. 7008–7012.
  7. Logistic regression, AdaBoost and Bregman distances. Machine Learning (2002).
  8. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).
  9. Social media popularity prediction: A multiple feature fusion approach with deep neural networks. In Proceedings of ACM international conference on Multimedia (ACM MM). 2682–2686.
  10. Nathan Ellering. 2016. What 16 Studies Say About The Best Times To Post On Social Media. http://coschedule.com/blog/best-times-to-post-on-social-media/. [Online].
  11. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770–778.
  12. Support vector machines. IEEE Intelligent Systems and their Applications (1998).
  13. Popularity prediction of social media based on multi-modal feature mining. In Proceedings of ACM international conference on Multimedia (ACM MM). 2687–2691.
  14. Rethinking relation between model stacking and recurrent neural networks for social media prediction. In Proceedings of ACM international conference on Multimedia (ACM MM). 4585–4589.
  15. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In Proceedings of the 31st International Conference on Neural Information Processing Systems.
  16. Ryota Kobayashi and Renaud Lambiotte. 2016. TiDeH Time-Dependent Hawkes process for predicting retweet dynamics. In arXiv preprint arXiv:1603.09449.
  17. Predicting bursts and popularity of hashtags in real-time. In Proceedings of the 37th international ACM SIGIR Conference on Research & development in Information Retrieval. 927–930.
  18. HyFea: Winning Solution to Social Media Popularity Prediction for Multimedia Grand Challenge 2020. In Proceedings of ACM international conference on Multimedia (ACM MM). 4565–4569.
  19. Classification and regression by randomForest. R news (2002), 18–22.
  20. RoBERTa: A Robustly Optimized BERT Pretraining Approach.
  21. Franck Michel. 2019. How many photos are uploaded monthly to Flickr? http://www.flickr.com/photos/franckmichel/6855169886. [Online].
  22. Efficient Estimation of Word Representations in Vector Space.
  23. Seth A Myers and Jure Leskovec. 2014. The bursty dynamics of the twitter information network. In Proceedings of the 23rd International Conference on World Wide Web. 913–924.
  24. Language models are unsupervised multitask learners. OpenAI blog (2019).
  25. Predictability of Popularity: Gaps between Prediction and Understanding. In Proceedings of AAAI Conference on Artificial Intelligence.
  26. Gabor Szabo and Bernardo A Huberman. 2010. Predicting the popularity of online content. Commun. ACM 53, 8 (2010), 80–88.
  27. Flickr team. 2019. Flickr API. https://www.flickr.com/services/api/. [Online].
  28. A Feature Generalization Framework for Social Media Popularity Prediction. In Proceedings of ACM international conference on Multimedia (ACM MM). 4570–4574.
  29. SMP Challenge: An Overview of Social Media Prediction Challenge 2019. In Proceedings of the 27th ACM International Conference on Multimedia.
  30. Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks. In International Joint Conference on Artificial Intelligence (IJCAI) (Melbourne, Australia).
  31. Unfolding Temporal Dynamics: Predicting Social Media Popularity Using Multi-scale Temporal Decomposition. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI) (Phoenix, Arizona).
  32. Deeply Exploit Visual and Language Information for Social Media Popularity Prediction. In Proceedings of the 30th ACM International Conference on Multimedia.
  33. Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition.
  34. Multimodal deep learning for social media popularity prediction with attention mechanism. In Proceedings of ACM international conference on Multimedia (ACM MM). 4580–4584.
  35. Participatory Cultural Mapping Based on Collective Behavior Data in Location-Based Social Networks. ACM Transactions on Intelligent Systems and Technology 7, 3, Article 30 (Jan. 2016), 30:1–30:23 pages.
  36. Jaewon Yang and Jure Leskovec. 2011. Patterns of Temporal Variation in Online Media. In Proceedings of ACM International Conference on Web Search and Data Mining (WSDM).
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (8)
  1. Bo Wu (144 papers)
  2. Peiye Liu (7 papers)
  3. Wen-Huang Cheng (40 papers)
  4. Bei Liu (63 papers)
  5. Zhaoyang Zeng (29 papers)
  6. Jia Wang (163 papers)
  7. Qiushi Huang (23 papers)
  8. Jiebo Luo (355 papers)
Citations (5)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets