A Generic Framework for Social Event Analysis

Abstract

Social event is something that occurs at specific place and time associated with some specific actions, and it consists of many stories over time. With the explosion of Web 2.0 platforms, a popular social event that is happening around us and around the world can spread very fast. As a result, social event analysis becomes more and more important for users to understand the whole evolutionary trend of social event over time. However, it is very challenging to do social event analysis because social event data from different social media sites have multi-modal, multi-domain, and large-scale properties. The goal of our research is to design advanced multimedia techniques to deal with the above issues and establish an effective and robust social event analysis framework for social event representation, detection, tracking and evolution analysis. (1) For social event representation, we propose a novel cross-domain collaborative learning algorithm based on non-parametric Bayesian dictionary learning model. It can make use of the shared domain priors and modality priors to collaboratively learn the data's representations by considering the domain discrepancy and the multi-modal property.(2) For social event detection, we propose a boosted multi-modal supervised Latent Dirichlet Allocation model. It can effectively exploit multi-modality information and utilize boosting weighted sampling strategy for large-scale data processing. (3) For social event tracking, we propose a novel multi-modal event topic model, which can effectively model the correlations between textual and visual modalities, and obtain their topics over time. (4) For social event evolution analysis, we propose a novel multi-modal multi-view topic-opinion mining model to conduct fined-grained topic and opinion analysis for social events from multiple social media sites collaboratively. It can discover multi-modal topics and the corresponding opinions over time to understand the evolutionary processes of social event. Extensive experimental results show that the proposed algorithms perform favorably against state-of-the-art methods for social event analysis.