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 …

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 …

Real-world social events typically exhibit a severe class-imbalance distribution, which
makes the trained detection model encounter a serious generalization challenge. Most …

The data-hungry problem, characterized by insufficiency and low-quality of data, poses
obstacles for deep learning models. Transfer learning has been a feasible way to transfer …

Social event detection refers to extracting relevant message clusters from social media data
streams to represent specific events in the real world. Social event detection is important in …

Detecting events from social media data streams is gradually attracting researchers. The
innate challenge for detecting events is to extract discriminative information from social …

Recently published graph neural networks (GNNs) show promising performance at social
event detection tasks. However, most studies are oriented toward monolingual data in …

Event extraction (EE) is a fundamental information extraction task that aims to identify
structured events, including event types, triggers and arguments, from unstructured texts …

Structural Entropy (SE) measures the structural information contained in a graph. Minimizing
or maximizing SE helps to reveal or obscure the intrinsic structural patterns underlying …

Social platforms have emerged as a crucial platform for disseminating and discussing
information about real-life events, which offers an excellent opportunity for early detection of …