Hierarchical and Incremental Structural Entropy Minimization for Unsupervised Social Event Detection

TL;DR

This paper introduces HISEvent, an unsupervised, graph-structural entropy minimization framework for social event detection that outperforms GNN-based methods without requiring labeled data or predefined event counts.

Contribution

HISEvent innovatively combines 1D and 2D structural entropy minimization to enhance message graph construction and event detection in an unsupervised manner.

Findings

HISEvent outperforms existing GNN-based methods in social event detection.

The framework achieves state-of-the-art results in both closed- and open-set scenarios.

It is efficient and robust across various experimental settings.

Abstract

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.