Graph Neural Networks (GNNs) have attracted tremendous attention by demonstrating their
capability to handle graph data. However, they are difficult to be deployed in resource …

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 …

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 …

State-of-the-art Graph Neural Networks (GNNs) have achieved tremendous success in
social event detection tasks when restricted to a closed set of events. However, considering …

Aspect-level sentiment classification (ASC) seeks to reveal the emotional tendency of a
designated aspect of a text. Some researchers have recently tried to exploit large amounts of …

Current research on online public opinion regarding the coronavirus disease in 2019
(COVID-19) leverages keyword extraction, sentiment analysis, and topic modeling to …

Training social event detection models through federated learning (FedSED) aims to
improve participants' performance on the task. However, existing federated learning …

Event detection in a city is crucial for the government to listen to the voice of the citizens, be
aware of the real occurrences in a city, and then make wiser policies. However, in reality …