Social Media Popularity Prediction
3 papers with code • 1 benchmarks • 1 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.
Benchmarks
These leaderboards are used to track progress in Social Media Popularity Prediction
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
Ranking social media news feed
Most implemented papers
Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks
With a joint embedding network, we obtain a unified deep representation of multi-modal user-post data in a common embedding space.
Ranking news feed updates on social media: A comparative study of supervised models
For this matter, supervised learning models have been commonly used to predict relevance.
Ranking Social Media News Feeds: A Comparative Study of Personalized and Non-personalized Prediction Models
In this work, to predict the relevance of news feed updates and improve user experience, we use the random forest algorithm to train and introduce a personalized prediction model for each user.
