Relational Prompt-based Pre-trained Language Models for Social Event Detection

2024/04/12 by Pu Li, Li, Pu, Xiaoyan Yu +13 · 2 citations
Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #Artificial Intelligence (cs.AI) #Complex Network Analysis Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Sentiment Analysis and Opinion Mining #Social and Information Networks (cs.SI)

paper · pdf · doi:10.48550/arxiv.2404.08263

openalex publication_date 2024/04/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Social Event Detection (SED) aims to identify significant events from social streams, and has a wide application ranging from public opinion analysis to risk management. In recent years, Graph Neural Network (GNN) based solutions have achieved state-of-the-art performance. However, GNN-based methods often struggle with missing and noisy edges between messages, affecting the quality of learned message embedding. Moreover, these methods statically initialize node embedding before training, which, in turn, limits the ability to learn from message texts and relations simultaneously. In this paper, we approach social event detection from a new perspective based on Pre-trained Language Models (PLMs), and present RPLM_SED (Relational prompt-based Pre-trained Language Models for Social Event Detection). We first propose a new pairwise message modeling strategy to construct social messages into message pairs with multi-relational sequences. Secondly, a new multi-relational prompt-based pairwise message learning mechanism is proposed to learn more comprehensive message representation from message pairs with multi-relational prompts using PLMs. Thirdly, we design a new clustering constraint to optimize the encoding process by enhancing intra-cluster compactness and inter-cluster dispersion, making the message representation more distinguishable. We evaluate the RPLM_SED on three real-world datasets, demonstrating that the RPLM_SED model achieves state-of-the-art performance in offline, online, low-resource, and long-tail distribution scenarios for social event detection tasks.

Cited by

• Graph Your Own Prompt
• Explicit and Implicit Data Augmentation for Social Event Detection

Related

• Transferring Knowledge Distillation for Multilingual Social Event Detection
• Towards Effective, Efficient and Unsupervised Social Event Detection in the Hyperbolic Space
• From Known to Unknown: Quality-aware Self-improving Graph Neural Network for Open Set Social Event Detection
• Hierarchical and Incremental Structural Entropy Minimization for Unsupervised Social Event Detection
• Evidential Temporal-aware Graph-based Social Event Detection via Dempster-Shafer Theory