MMF: Winning Solution to Social Media Popularity Prediction Challenge 2024

摘要

The Social Media Prediction (SMP) challenge focuses on predicting the popularity of an online post in social media. Social media contains multimodal information, such as texts, images, user IDs, timestamps, locations, etc. Many studies have presented various feature extraction methods to retrieve the multimodal features and used the retrieved features to predict the popularity of posts. However, they often overlook the phenomenon that the posts made by the same user tend to have similar popularity. In this paper, we propose the MultiModal Framework, called MMF, our winning solution to the Social Media Prediction (SMP) Challenge 2024. MMF derives the post representations by considering the interaction relationships and inter-relationships among the extracted multimodal features. Also, it introduces the pseudo labels to consider the phenomenon that the posts made by the same user tend to have similar popularity and learn the popularity distributions of users in the test dataset. Therefore, MMF can simultaneously learn the relationships between post representations and true labels from the data of the training dataset, and users' popularity distributions from the data of the test dataset. The extensive experiments on the Social Media Prediction Dataset show that our proposed framework outperforms the compared models in terms of Spearman's rank correlation and mean absolute error.