Multi-Modal Supervised Latent Dirichlet Allocation for Event Classification in Social Media

Abstract

In social media, many existing websites (e.g., Flickr, YouTube, and Facebook) are for users to share their own interests and opinions of many popular events, and successfully facilitate the event generation, sharing and propagation. As a result, there are substantial amounts of user-contributed media data (e.g., images, videos, and textual content) for a wide variety of real-world events of different types and scales. The aim of this paper is to automatically identify the interesting events from massive social media data, which are useful to browse, search and monitor social events by users or governments. To achieve this goal, we propose a novel multi-modal supervised latent dirichlet allocation (mm-SLDA) for social event classification. Our proposed mm-SLDA has a number of advantages. (1) It can effectively exploit the multi-modality and the multi-class property of social events jointly. (2) It makes use of the supervised social event category label information and is able to classify multi-class social event directly. We evaluate our proposed mm-SLDA on a real world dataset and show extensive experimental results, which demonstrate that our model outperforms state-of-the-art methods.