  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 noisy and missing 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.
