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Related Experiment Video

Updated: Sep 16, 2025

Measuring Neural and Behavioral Activity During Ongoing Computerized Social Interactions: An Examination of Event-Related Brain Potentials

Measuring Neural and Behavioral Activity During Ongoing Computerized Social Interactions: An Examination of Event-Related Brain Potentials

Published on: November 15, 2014

PromptSED: An evolving topic-enhanced prompting framework for incremental social event detection.

Xiaoyan Yu¹, Jiaqian Ren², Lei Jiang³

¹School of Computer Science and Technology, Beijing Institute of Technology, 100081, Beijing, China.

Neural Networks : the Official Journal of the International Neural Network Society

|July 11, 2025

Summary

This summary is machine-generated.

PromptSED enhances social event detection (SED) using an evolving topic-enhanced prompt learning framework. This method dynamically tracks topics and injects them into pre-trained language models (PLMs) for improved incremental event identification.

Keywords:

Pre-trained language models Prompt-based fine-tuning Social event detection

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Area of Science:

Natural Language Processing
Artificial Intelligence
Social Media Analytics

Background:

Transformer-based pre-trained language models (PLMs) show promise for social event detection (SED).
SED faces challenges with short, noisy social media text and continuously emerging events.
Existing methods struggle with the dynamic nature of social data streams.

Purpose of the Study:

To introduce PromptSED, an evolving topic-enhanced prompt learning framework for incremental social event detection.
To address the limitations of PLMs in handling noisy, evolving social data for SED.
To enable training-free, dynamic topic tracking and event detection in social streams.

Main Methods:

PromptSED employs selective topic injection, converting evolving topics into prompts for PLMs.
A noise-tolerant optimization strategy improves resilience and event differentiation.
Training-free topic tracking facilitates incremental SED without additional labeling.

Main Results:

PromptSED achieved significant performance improvements in social event detection on two public datasets.
Component analysis validated the effectiveness of selective topic injection, noise-tolerant optimization, and training-free topic tracking.
Comparative analysis evaluated the applicability of decoder-only large language models for SED.

Conclusions:

PromptSED offers an effective framework for incremental social event detection, overcoming challenges in social data.
The proposed methods enhance the adaptability and robustness of PLMs for dynamic event identification.
The framework demonstrates strong performance and potential for real-world social media event monitoring.