Hierarchical and Incremental Structural Entropy Minimization for Unsupervised Social Event Detection
This addresses the problem of automating social event detection for applications like social media analysis, offering an unsupervised and efficient alternative to GNN-based methods.
The paper tackles social event detection by proposing an unsupervised framework, HISEvent, that uses graph structural entropy minimization to construct informative message graphs and detect events without requiring manual labeling or predetermined event numbers, achieving state-of-the-art performance in both closed- and open-set settings.
As a trending approach for social event detection, graph neural network (GNN)-based methods enable a fusion of natural language semantics and the complex social network structural information, thus showing SOTA performance. However, GNN-based methods can miss useful message correlations. Moreover, they require manual labeling for training and predetermining the number of events for prediction. In this work, we address social event detection via graph structural entropy (SE) minimization. While keeping the merits of the GNN-based methods, the proposed framework, HISEvent, constructs more informative message graphs, is unsupervised, and does not require the number of events given a priori. Specifically, we incrementally explore the graph neighborhoods using 1-dimensional (1D) SE minimization to supplement the existing message graph with edges between semantically related messages. We then detect events from the message graph by hierarchically minimizing 2-dimensional (2D) SE. Our proposed 1D and 2D SE minimization algorithms are customized for social event detection and effectively tackle the efficiency problem of the existing SE minimization algorithms. Extensive experiments show that HISEvent consistently outperforms GNN-based methods and achieves the new SOTA for social event detection under both closed- and open-set settings while being efficient and robust.