Effective and Unsupervised Social Event Detection and Evolution via RAG and Structural Entropy

TL;DR

RagSEDE is an unsupervised foundation model that improves social event detection and evolution modeling by using a novel sampling strategy, retrieval-augmented generation, and structural information theory, addressing key challenges in social media analysis.

Contribution

It introduces a new unsupervised framework combining RAG and structural entropy to enhance event detection and evolution modeling in social media.

Findings

Outperforms existing methods on public datasets

Effectively reduces noise and computational costs

Accurately models event evolution over time

Abstract

With the growing scale of social media, social event detection and evolution modeling have attracted increasing attention. Graph neural networks (GNNs) and transformer-based pre-trained language models (PLMs) have become mainstream approaches in this area. However, existing methods still face three major challenges. First, the sheer volume of social media messages makes learning resource-intensive. Second, the fragmentation of social media messages often impedes the model's ability to capture a comprehensive view of the events. Third, the lack of structured temporal context has hindered the development of effective models for event evolution, limiting users' access to event information. To address these challenges, we propose a foundation model for unsupervised Social Event Detection and Evolution, namely RagSEDE. Specifically, RagSEDE introduces a representativeness- and diversity-driven sampling strategy to extract key messages from massive social streams, significantly reducing noise and computational overhead. It further establishes a novel paradigm based on Retrieval Augmented Generation (RAG) that enhances PLMs in detecting events while simultaneously constructing and maintaining an evolving event knowledge base. Finally, RagSEDE leverages structural information theory to dynamically model event evolution keywords for the first time. Extensive experiments on two public datasets demonstrate the superiority of RagSEDE in open-world social event detection and evolution.