Boosted Multi-modal Supervised Latent Dirichlet Allocation for Social Event Classification

Abstract

With the rapidly increasing popularity of Social Media sites (e.g., Flickr, YouTube, and Facebook), it is convenient for users to share their own comments on many social events, which successfully facilitates social event generation, sharing and propagation and results in a large amount of user-contributed media data (e.g., images, videos, and texts) for a wide variety of real-world events of different types and scales. As a consequence, it has become more and more difficult to find exactly the interesting events from massive social media data, which is useful to browse, search and monitor social events by users or governments. To deal with these issues, we propose a novel boosted multi-modal supervised Latent Dirichlet Allocation (BMM-SLDA) for social event classification. Our BMM-SLDA has a number of advantages. (1) It can effectively exploit the multi-modality and the supervised information of social events jointly. (2) It is suitable to large-scale data analysis by utilizing boosting weighted sampling strategy to iteratively select a small subset data to efficiently train the corresponding topic models. (3) It effectively exploits boosting document weight distribution by classification error, and can iteratively learn new topic model to correct the previously misclassified documents. We evaluate our BMM-SLDA on a real-world dataset and show extensive results, which show that our model outperforms state-of-the-art methods.