time-series-7
Social Media Popularity Prediction
3260 papers • 126 benchmarks • 313 datasets
Social Media Popularity Prediction (SMPP) aims to predict the future popularity (e.g., clicks, views, likes, etc.) of online posts automatically via plenty of social media data from public platforms. It is a crucial problem for social media learning and forecasting and one of the most challenging problems in the field. With the ever-changing user interests and public attention on social media platforms, how to predict popularity accurately becomes more challenging than before. This task is valuable to content providers, marketers, or consumers in a range of real-world applications, including multimedia advertising, recommendation system, or trend analysis.
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Most implemented papers
Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks
Yongdong Zhang, Jintao Li, Wen-Huang Cheng, Qiushi Huang, Tao Mei, Bo Wu•Mon Dec 11 2017
A novel prediction framework called Deep Temporal Context Networks (DTCN) is proposed, which outperforms state-of-the-art deep prediction algorithms, and is designed to predict new popularity with temporal coherence across multiple time-scales.
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